{"meta":{"query_hash":"02f81285c820","filters":{"venue":"Computational Biology and Chemistry"},"cohort_total":52,"direct_labels_cover":0,"predictions_cover":52,"exported":52,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/02f81285c820","api":"https://metacan.xera.ac/api/v1/cohort?venue=Computational+Biology+and+Chemistry"},"results":[{"id":"W1966621989","doi":"10.1016/j.compbiolchem.2013.08.008","title":"In vitro cytotoxicity assessment based on KC50 with real-time cell analyzer (RTCA) assay","year":2013,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health; University of Alberta","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Natural Sciences and Engineering Research Council of Canada; National Research Council Canada; National Natural Science Foundation of China; Alberta Health; National Science Foundation","keywords":"Cytotoxicity; In vitro; Spectrum analyzer; Chemistry; Computer science; Biochemistry","score_opus":0.006806728328071241,"score_gpt":0.2757529008248103,"score_spread":0.2689461724967391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966621989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74737394,0.011673418,0.18297356,0.0010337919,0.0017332076,0.002369684,0.011960506,0.0048330487,0.036048837],"genre_scores_gemma":[0.79681486,0.00901881,0.14865793,0.00078073284,0.00034953785,0.005593283,0.009368188,0.00035144945,0.0290651],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99576086,0.000840105,0.00068262155,0.00077766215,0.0015249266,0.0004139097],"domain_scores_gemma":[0.9982193,0.000320007,0.00024817075,0.0002921744,0.00081942143,0.00010090366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016973959,0.0026945088,0.0025605848,0.002083625,0.0012166046,0.0010264629,0.0017006204,0.0011506786,0.005148489],"category_scores_gemma":[0.0009036715,0.00070720376,0.001725515,0.0029040496,0.0009497676,0.0010216511,0.00078159914,0.0029831196,0.0024327207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009290247,0.0010533319,0.002789631,0.0007724987,0.00015206326,0.00021071783,0.00044659153,0.001593063,0.97249246,0.00088801945,0.0022079344,0.016464584],"study_design_scores_gemma":[0.00006432546,0.001839327,0.0036000651,0.00002579169,0.00019877969,0.00020585365,0.00010035635,0.0048516276,0.9856333,0.00025603952,0.0031455832,0.00007884696],"about_ca_topic_score_codex":0.0017213877,"about_ca_topic_score_gemma":0.0032550106,"teacher_disagreement_score":0.005148489,"about_ca_system_score_codex":0.0006066218,"about_ca_system_score_gemma":0.001015212,"threshold_uncertainty_score":0.017223477},"labels":[],"label_agreement":null},{"id":"W1967761094","doi":"10.1016/j.compbiolchem.2015.04.008","title":"Systematic investigation of sequence and structural motifs that recognize ATP","year":2015,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sequence motif; Structural motif; Nucleotide; Aminoacyl tRNA synthetase; Biochemistry; ATP hydrolysis; Adenosine triphosphate; Biology; Protein Data Bank (RCSB PDB); Computational biology; Enzyme; Transfer RNA; RNA; ATPase; Gene","score_opus":0.030700733196844958,"score_gpt":0.2644670454981234,"score_spread":0.23376631230127842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967761094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916671,0.00053835433,0.0064528817,0.00003684614,0.000007769765,0.00003256334,0.00027987326,0.00008235858,0.0009021914],"genre_scores_gemma":[0.98915267,0.00033846148,0.009508106,0.000028135164,0.0000058306505,0.000020144089,0.00054967066,0.00002490739,0.00037217487],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990714,0.000018972572,0.000008488054,0.000025241468,0.000024667854,0.000015517116],"domain_scores_gemma":[0.9995253,0.0002019414,0.00010345553,0.000079841004,0.000055450586,0.000034093602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021085411,0.00021457797,0.00029653273,0.00036352457,0.00022025369,0.0003172162,0.000342047,0.00025378133,0.0007117786],"category_scores_gemma":[0.00066933705,0.00021215442,0.0002127382,0.00039790742,0.00022380867,0.00021911235,0.00019645986,0.0004711717,0.00018016761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040255146,0.0001131721,0.0048644347,0.00019942761,0.000038932678,0.0001799053,0.000043585776,0.0025518627,0.9734114,0.0012140848,0.00015159056,0.016829034],"study_design_scores_gemma":[0.00005296834,0.00060202053,0.01244342,0.000022948976,0.00013508149,0.00060371554,0.000121824676,0.029348237,0.95145017,0.0011643553,0.004036604,0.000018656283],"about_ca_topic_score_codex":0.00039092405,"about_ca_topic_score_gemma":0.0009832835,"teacher_disagreement_score":0.0007117786,"about_ca_system_score_codex":0.00017550476,"about_ca_system_score_gemma":0.00030224348,"threshold_uncertainty_score":0.0023810863},"labels":[],"label_agreement":null},{"id":"W1970603373","doi":"10.1016/j.compbiolchem.2003.08.001","title":"The DNA double helix fifty years on","year":2003,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"DNA and Nucleic Acid Chemistry","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"DNA; Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid; Double stranded; Helix (gastropod); Watson; Biology; Chemistry; Base pair; History; Genetics; Computer science; Paleontology; Artificial intelligence","score_opus":0.0056996541915671495,"score_gpt":0.2427464620208225,"score_spread":0.23704680782925536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970603373","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018442016,0.74749184,0.015614502,0.09153872,0.037822515,0.00003637113,0.00033628067,0.00024776655,0.088470116],"genre_scores_gemma":[0.26443925,0.5113579,0.017749885,0.056359626,0.028135868,0.000104429964,0.0007158622,0.00048817607,0.12064893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922466,0.0002481631,0.00004520002,0.00018774431,0.00017987944,0.000114436625],"domain_scores_gemma":[0.99750656,0.0011619607,0.00013942443,0.0002878568,0.0005992657,0.00030483518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019756379,0.0005384871,0.00072116754,0.0010547839,0.0009981167,0.0027553744,0.0007610244,0.002099754,0.0070694056],"category_scores_gemma":[0.004886648,0.0004201763,0.000480984,0.0009863906,0.004295597,0.004887451,0.0031138684,0.0035598068,0.0024947203],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052902324,0.00007519628,0.0010252498,0.0016156386,0.00008809548,0.00028451433,0.0011906872,0.0027833616,0.0034533453,0.51622796,0.09242966,0.38029727],"study_design_scores_gemma":[0.000016441601,0.00004630083,0.00024582905,0.0005141061,0.00001473706,0.00011166457,0.0001633284,0.00042553988,0.0011909059,0.048372462,0.94887435,0.000024394029],"about_ca_topic_score_codex":0.0023144432,"about_ca_topic_score_gemma":0.002082617,"teacher_disagreement_score":0.0070694056,"about_ca_system_score_codex":0.0024757253,"about_ca_system_score_gemma":0.001647471,"threshold_uncertainty_score":0.023649514},"labels":[],"label_agreement":null},{"id":"W1976757804","doi":"10.1016/j.compbiolchem.2011.02.001","title":"Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources","year":2011,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cytotoxicity; Toxicant; Computer science; Support vector machine; Biological system; Identification (biology); Data mining; Chemistry; Artificial intelligence; Toxicity; Biology; Biochemistry; In vitro; Ecology","score_opus":0.03558577187458409,"score_gpt":0.2235328163591097,"score_spread":0.18794704448452562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976757804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89248353,0.00041055406,0.1018785,0.00080321246,0.000093462615,0.00008675956,0.001769479,0.00055010663,0.0019244953],"genre_scores_gemma":[0.9903932,0.000116473784,0.008201436,0.000038902668,0.000011565022,0.000056791912,0.00053442956,0.000022057304,0.0006250743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967647,0.00009122456,0.000033511253,0.00008288888,0.00007152027,0.000044387532],"domain_scores_gemma":[0.9966509,0.0024949808,0.00021574638,0.00012875668,0.00040512133,0.00010451944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011166906,0.00081345846,0.0009331782,0.0007518543,0.0004395068,0.0013381145,0.0012198102,0.0016246182,0.0007857711],"category_scores_gemma":[0.0047431104,0.0006668234,0.001109558,0.0006825491,0.0005965053,0.0012029897,0.0005244321,0.0008353808,0.00014260608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017582028,0.000013266499,0.00076574687,0.000009589332,0.000007911106,0.000010437353,0.0000023965308,0.9981877,0.00012172091,0.00011987746,0.00003530547,0.00070843176],"study_design_scores_gemma":[0.0000021415826,0.0000053642984,0.00007811542,5.389627e-7,0.000002206079,0.0000014204435,0.0000014937173,0.9995415,0.00018963891,0.00016138218,0.000015190708,9.77379e-7],"about_ca_topic_score_codex":0.0408211,"about_ca_topic_score_gemma":0.018732678,"teacher_disagreement_score":0.0408211,"about_ca_system_score_codex":0.0020130677,"about_ca_system_score_gemma":0.0017839294,"threshold_uncertainty_score":0.08116698},"labels":[],"label_agreement":null},{"id":"W1987137880","doi":"10.1016/j.compbiolchem.2013.12.004","title":"Mode of action classification of chemicals using multi-concentration time-dependent cellular response profiles","year":2014,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Mode of action; Principal component analysis; Cytotoxic T cell; Computer science; Pattern recognition (psychology); Artificial intelligence; Chemistry; Toxicology; Biology; In vitro; Biochemistry","score_opus":0.03806880676217871,"score_gpt":0.34455676957951586,"score_spread":0.30648796281733715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987137880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88118595,0.0016486922,0.10937129,0.00036600954,0.00009170086,0.00027640417,0.002979332,0.0011166264,0.0029640002],"genre_scores_gemma":[0.98023945,0.0006134927,0.016932227,0.00008626689,0.000022386846,0.00008412046,0.000829968,0.000026647864,0.0011653353],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997911,0.000030858922,0.00001945947,0.00005622692,0.00006436932,0.000038043225],"domain_scores_gemma":[0.99902475,0.0004903621,0.00017087179,0.00008298546,0.00017185266,0.000059098023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005852489,0.00070414995,0.0007756157,0.001914696,0.00018357107,0.0008287205,0.00045627978,0.0007387555,0.0014725451],"category_scores_gemma":[0.0014747841,0.00017195888,0.0013665487,0.00081906485,0.0002484979,0.00047838895,0.0003309664,0.00062750047,0.0005523881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005218669,0.0008766558,0.057092693,0.0008678119,0.00047304618,0.0005347635,0.00009422188,0.08909573,0.67831063,0.0019791499,0.0010807739,0.1643759],"study_design_scores_gemma":[0.00007726129,0.0015766594,0.05458003,0.000038584272,0.0004498155,0.0006688681,0.00009410777,0.7473704,0.19135062,0.0025689593,0.0011176529,0.00010703318],"about_ca_topic_score_codex":0.00147176,"about_ca_topic_score_gemma":0.0012775128,"teacher_disagreement_score":0.001914696,"about_ca_system_score_codex":0.00052931777,"about_ca_system_score_gemma":0.0004737441,"threshold_uncertainty_score":0.0049260855},"labels":[],"label_agreement":null},{"id":"W1989886744","doi":"10.1016/j.compbiolchem.2007.11.001","title":"Molecular dynamics simulation study on the structural stabilities of polyglutamine peptides","year":2007,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genetic Neurodegenerative Diseases","field":"Neuroscience","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Molecular dynamics; Huntingtin; Chemistry; Hydrogen bond; Crystallography; Helix (gastropod); Huntingtin Protein; Protein structure; Biophysics; Molecule; Biology; Biochemistry; Computational chemistry; Gene","score_opus":0.02005196045933731,"score_gpt":0.3042949525023854,"score_spread":0.2842429920430481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989886744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986649,0.00045081932,0.004892226,0.000608607,0.00006832068,0.000027455633,0.00020822635,0.000072961026,0.0070223743],"genre_scores_gemma":[0.9965724,0.00018321098,0.0022851033,0.000064333406,0.00001639908,0.000036711805,0.00016282323,0.000026437225,0.00065269036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998914,0.00002820414,0.000004106252,0.000017121496,0.000022436583,0.00003681377],"domain_scores_gemma":[0.99918765,0.00045082584,0.0000580861,0.000050681963,0.00014107663,0.000111664034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036844227,0.0006115189,0.00087655697,0.0006051458,0.0013258164,0.0004983924,0.00085986813,0.00077903044,0.0024711792],"category_scores_gemma":[0.001380932,0.0004048713,0.0007138797,0.0005967441,0.0008287963,0.00076772756,0.00046272532,0.0009786577,0.00014477248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002955065,0.00022173718,0.0033268842,0.00009157499,0.00010758873,0.00027205216,0.00014579247,0.97741276,0.004195477,0.010219841,0.0009086803,0.0028021012],"study_design_scores_gemma":[0.000057038356,0.00004436364,0.00063521625,0.0000051979723,0.00001505689,0.000012486958,0.000027465925,0.997186,0.0006128229,0.0012218936,0.00017452288,0.00000789007],"about_ca_topic_score_codex":0.019052504,"about_ca_topic_score_gemma":0.009493552,"teacher_disagreement_score":0.019052504,"about_ca_system_score_codex":0.0009908287,"about_ca_system_score_gemma":0.0012954848,"threshold_uncertainty_score":0.037883222},"labels":[],"label_agreement":null},{"id":"W1994596768","doi":"10.1016/j.compbiolchem.2005.10.003","title":"Construction and characterization of a rock-cluster-based EST analysis pipeline","year":2005,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Major State Basic Research Development Program of China; National Key Research and Development Program of China; Wuhan University; Institute of Genetics; National Natural Science Foundation of China","keywords":"Pipeline (software); Perl; Computer science; Cluster analysis; Identification (biology); Cluster (spacecraft); Annotation; Construct (python library); Data mining; Computational biology; Artificial intelligence; Operating system; Biology; Programming language","score_opus":0.00545967331150612,"score_gpt":0.24625304526739733,"score_spread":0.2407933719558912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994596768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05225874,0.00007013255,0.78097045,0.00028657733,0.00007618278,0.00096938247,0.011580467,0.15160564,0.0021823666],"genre_scores_gemma":[0.12172151,0.00006683692,0.8038443,0.00018883088,0.000035710156,0.0013932994,0.058033824,0.009073045,0.005642618],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884135,0.00013450225,0.00012480063,0.00035022502,0.00035913353,0.00019008185],"domain_scores_gemma":[0.9967188,0.0008173595,0.00015648257,0.00085888105,0.001167592,0.0002808393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024578425,0.0012201658,0.0010927968,0.0021958922,0.00166302,0.0016797563,0.0026239294,0.00064070337,0.00838605],"category_scores_gemma":[0.0053427946,0.0009098099,0.0017800228,0.0021830052,0.00058203924,0.0014520923,0.0018734355,0.0017476792,0.0068820436],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054735853,0.0010378875,0.019074488,0.0009970574,0.00046841108,0.0008067536,0.0011551032,0.039123856,0.36961538,0.01211203,0.10727184,0.44286355],"study_design_scores_gemma":[0.0005490021,0.00047012008,0.012247481,0.000036405418,0.00020170269,0.00028193928,0.00028207514,0.6436725,0.28960982,0.005857374,0.046583153,0.00020847488],"about_ca_topic_score_codex":0.009968298,"about_ca_topic_score_gemma":0.009804617,"teacher_disagreement_score":0.009968298,"about_ca_system_score_codex":0.0010534804,"about_ca_system_score_gemma":0.004722563,"threshold_uncertainty_score":0.028054118},"labels":[],"label_agreement":null},{"id":"W2009847553","doi":"10.1016/j.compbiolchem.2008.01.001","title":"Water-mediated interactions in the CRP–cAMP–DNA complex: Does water mediate sequence-specific binding at the DNA primary-kink site?","year":2008,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Core Research for Evolutional Science and Technology; Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrogen bond; DNA; Base pair; Chemistry; Crystallography; Sequence (biology); DNA sequencing; Base (topology); Molecular dynamics; Binding site; Escherichia coli; Molecule; Biophysics; Stereochemistry; Biology; Biochemistry; Computational chemistry; Gene; Organic chemistry","score_opus":0.02121107132538371,"score_gpt":0.24871490472500418,"score_spread":0.22750383339962046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009847553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9853247,0.00034335916,0.01100221,0.00047770437,0.000047680544,0.000022339962,0.000054375465,0.000085487525,0.002642106],"genre_scores_gemma":[0.9986136,0.000122118,0.00074286235,0.0000506091,0.0000060201296,0.000008015644,0.000021999644,0.00001094488,0.00042380398],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997211,0.00003508256,0.000008529593,0.000048238297,0.00005032059,0.00013671746],"domain_scores_gemma":[0.99957424,0.0001907967,0.00005314163,0.000037832997,0.000048006623,0.00009596651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039986285,0.00037445078,0.00074036483,0.00013031781,0.0005643051,0.0008020327,0.00093725964,0.00077750377,0.0030234666],"category_scores_gemma":[0.0016749437,0.00030502595,0.00030374128,0.00016083173,0.0010343682,0.0019643398,0.0007227778,0.0007197699,0.00032316835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00101792,0.00022325927,0.0084025515,0.00063326355,0.00012182814,0.0011258674,0.0009259464,0.04208737,0.8953914,0.027089896,0.0014442616,0.021536414],"study_design_scores_gemma":[0.00034471878,0.0006168635,0.013816035,0.000062479914,0.00013619603,0.00045714705,0.0021053527,0.42539182,0.52296966,0.029522266,0.004361785,0.00021568516],"about_ca_topic_score_codex":0.003844092,"about_ca_topic_score_gemma":0.003967379,"teacher_disagreement_score":0.003844092,"about_ca_system_score_codex":0.00046350775,"about_ca_system_score_gemma":0.0006449472,"threshold_uncertainty_score":0.010114551},"labels":[],"label_agreement":null},{"id":"W2010630721","doi":"10.1016/j.compbiolchem.2004.11.003","title":"Assessment of chemical libraries for their druggability","year":2005,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University; Montreal General Hospital; Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Druggability; Virtual screening; Metric (unit); Function (biology); Computer science; Chemical database; Rank (graph theory); Drug discovery; Data mining; Computational biology; Information retrieval; Chemistry; Bioinformatics; Mathematics; Biology; Biochemistry; Engineering","score_opus":0.017580961192713372,"score_gpt":0.32629576203244004,"score_spread":0.3087148008397267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010630721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9525619,0.0072054258,0.021745918,0.00051568175,0.00006236921,0.00041645943,0.00519763,0.0013877105,0.010906862],"genre_scores_gemma":[0.96110135,0.0029789105,0.027100123,0.00013718789,0.000035570312,0.00020348257,0.0052450355,0.00017322999,0.003025109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909997,0.00028194304,0.00007946503,0.00010428103,0.00035519397,0.00007927641],"domain_scores_gemma":[0.99528825,0.0026946971,0.0004706327,0.00044755172,0.00087232946,0.00022650005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018452589,0.00077547337,0.00087753305,0.0043628253,0.00059827464,0.0015650898,0.0005809715,0.00061806804,0.0050144936],"category_scores_gemma":[0.0090136025,0.0002928176,0.00079909276,0.0024716246,0.00036624723,0.0012979852,0.0007652463,0.0004881353,0.0010258714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010311159,0.0025312118,0.02001632,0.0016490248,0.00103693,0.00042516398,0.0002498578,0.13993435,0.36591968,0.006463639,0.0041432604,0.44731948],"study_design_scores_gemma":[0.000828769,0.0150397755,0.01630505,0.00017629181,0.0015410142,0.0008912363,0.00032650237,0.2939604,0.6486478,0.008535954,0.013563448,0.00018385],"about_ca_topic_score_codex":0.00076609926,"about_ca_topic_score_gemma":0.0013497467,"teacher_disagreement_score":0.0050144936,"about_ca_system_score_codex":0.0006331004,"about_ca_system_score_gemma":0.00076375064,"threshold_uncertainty_score":0.016775131},"labels":[],"label_agreement":null},{"id":"W2024446077","doi":"10.1016/s1476-9271(02)00089-0","title":"Quantitative relationships between molecular structures, environmental temperatures and octanol–air partition coefficients of polychlorinated biphenyls","year":2003,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Ministry of Education, India; Ministry of Earth Sciences","keywords":"Partition coefficient; Partition (number theory); Environmental chemistry; Octanol; Chemistry; Chromatography; Mathematics","score_opus":0.01131942848950818,"score_gpt":0.24492580840893663,"score_spread":0.23360637991942845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024446077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965301,0.00013056646,0.002771533,0.000048327056,0.0000027797598,0.0000031785994,0.00020376609,0.000032895652,0.00027684448],"genre_scores_gemma":[0.9989737,0.000056237026,0.00058700226,0.000008838686,0.0000018574375,0.0000039634024,0.00023973729,0.000006083117,0.00012254069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986565,0.000053769465,0.0000052372366,0.000028869099,0.000029148292,0.000017246355],"domain_scores_gemma":[0.9953029,0.003760199,0.00049010845,0.00015902678,0.0002254917,0.00006225416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005706626,0.00024060914,0.0001636258,0.00026236236,0.00010596972,0.00046622346,0.0002096405,0.0002773019,0.00052203896],"category_scores_gemma":[0.004334903,0.00024238115,0.00017517942,0.00020498167,0.00027358808,0.00048111472,0.00013735189,0.00030417248,0.00008932199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020385196,0.00032431976,0.1272429,0.00020630083,0.0003881244,0.000099592784,0.00007081562,0.70994765,0.13868998,0.0037642892,0.0006365109,0.016591016],"study_design_scores_gemma":[0.000047821184,0.00020464124,0.044248514,0.0000052468004,0.00009865472,0.000037986025,0.00003663596,0.877143,0.07592504,0.0019664594,0.00026338402,0.000022539698],"about_ca_topic_score_codex":0.0027577141,"about_ca_topic_score_gemma":0.0036583568,"teacher_disagreement_score":0.0027577141,"about_ca_system_score_codex":0.00037805465,"about_ca_system_score_gemma":0.000293164,"threshold_uncertainty_score":0.0054833293},"labels":[],"label_agreement":null},{"id":"W2029970659","doi":"10.1016/j.compbiolchem.2007.02.010","title":"Effect of internal viscosity on Brownian dynamics of DNA molecules in shear flow","year":2007,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Rheology and Fluid Dynamics Studies","field":"Chemical Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Brownian dynamics; Viscosity; Brownian motion; Weissenberg number; Shear flow; Dissipative system; Flow (mathematics); Dissipative particle dynamics; Statistical physics; Thermodynamics; Mechanics; Classical mechanics; Chemistry; Physics; Polymer","score_opus":0.0028229562592336907,"score_gpt":0.23561476513017718,"score_spread":0.2327918088709435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029970659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99596316,0.00042446196,0.0022135128,0.000104540995,0.000056491233,0.000004702566,0.000025203992,0.000048277874,0.0011596733],"genre_scores_gemma":[0.99879575,0.00019814947,0.0006182845,0.000020923877,0.000023414528,0.0000029358669,0.000021872225,0.000039047947,0.0002795179],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981457,0.00004333267,0.000008493171,0.000033360677,0.000038286013,0.00006209498],"domain_scores_gemma":[0.99751127,0.0016576202,0.00022867032,0.00009825838,0.00014613729,0.00035803963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053479505,0.00035240516,0.0004947263,0.0006429572,0.00059414236,0.0012021449,0.00025696185,0.00037960216,0.0017305501],"category_scores_gemma":[0.0036920388,0.0003107266,0.00036785248,0.00023187007,0.000985098,0.00082746503,0.00044705794,0.00095737923,0.0001511229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027548273,0.00033644718,0.0041437997,0.00029598095,0.00012956372,0.000668388,0.0006111379,0.029791784,0.93944365,0.004072934,0.00046663822,0.017284788],"study_design_scores_gemma":[0.00014337637,0.0009882841,0.01193452,0.00005848132,0.00024239927,0.00021917043,0.00015390682,0.18558949,0.79873925,0.0009281857,0.0008921697,0.00011074239],"about_ca_topic_score_codex":0.00077473995,"about_ca_topic_score_gemma":0.00060088997,"teacher_disagreement_score":0.0017305501,"about_ca_system_score_codex":0.00039992272,"about_ca_system_score_gemma":0.00022300263,"threshold_uncertainty_score":0.00578928},"labels":[],"label_agreement":null},{"id":"W2036747135","doi":"10.1016/j.compbiolchem.2011.07.005","title":"A degree-distribution based hierarchical agglomerative clustering algorithm for protein complexes identification","year":2011,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Hierarchical clustering; Partition (number theory); Hierarchical clustering of networks; Computer science; Cluster analysis; Measure (data warehouse); Data mining; Identification (biology); Hierarchical organization; Degree (music); Algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematics; Fuzzy clustering; Canopy clustering algorithm; Biology","score_opus":0.02156164124581855,"score_gpt":0.25756478158865526,"score_spread":0.2360031403428367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036747135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008048552,0.00014068215,0.98945564,0.00011019367,0.00003412222,0.00013002947,0.00017108783,0.0014611243,0.00044856014],"genre_scores_gemma":[0.046531633,0.00011167455,0.9501517,0.000058010355,0.000018134098,0.00019565018,0.0007577718,0.00032312112,0.001852324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984124,0.00032296366,0.00012658842,0.0003253698,0.0006732098,0.0001394365],"domain_scores_gemma":[0.9983505,0.00045958834,0.00010552899,0.00030543632,0.0006698396,0.00010907662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017761885,0.0011730588,0.0021052484,0.003011752,0.0031131536,0.0016583778,0.003866817,0.0016507155,0.00249841],"category_scores_gemma":[0.004012711,0.0011227807,0.0020330062,0.003642154,0.00094590744,0.0017915971,0.0022756648,0.0017452138,0.0019934478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006122154,0.0004398974,0.0033987404,0.00036011776,0.00060593575,0.00020102035,0.00065184716,0.24009423,0.022004219,0.018752005,0.014994373,0.69788533],"study_design_scores_gemma":[0.000059150123,0.000037243804,0.0011371563,0.000013381161,0.00006730559,0.00013639934,0.000073646945,0.9791797,0.004760097,0.010590014,0.0038989007,0.000046954625],"about_ca_topic_score_codex":0.020262778,"about_ca_topic_score_gemma":0.028702179,"teacher_disagreement_score":0.020262778,"about_ca_system_score_codex":0.0017140599,"about_ca_system_score_gemma":0.0038497674,"threshold_uncertainty_score":0.0402897},"labels":[],"label_agreement":null},{"id":"W2041702814","doi":"10.1016/j.compbiolchem.2007.08.005","title":"A hybrid Bayesian network learning method for constructing gene networks","year":2007,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Computer science; Artificial intelligence; Data mining; Machine learning; Bayesian probability; Dependency (UML); Constraint (computer-aided design); DNA microarray; Gene; Mathematics; Gene expression","score_opus":0.013428102111690984,"score_gpt":0.2926164758761844,"score_spread":0.2791883737644934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041702814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078779785,0.00005520819,0.9986118,0.000041517313,0.000009689537,0.000017222459,0.000053937165,0.00020614621,0.00021662323],"genre_scores_gemma":[0.03793876,0.00015398915,0.95924497,0.00011298204,0.000058016685,0.00021057398,0.00039903063,0.00014882543,0.0017327069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998439,0.0006577742,0.00006576659,0.00032193103,0.0004548944,0.000060614537],"domain_scores_gemma":[0.9957104,0.0030568896,0.00016809284,0.00038425977,0.0005559343,0.00012438143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029862127,0.00086483604,0.0016621295,0.0022541948,0.0010046647,0.0015780667,0.0033806763,0.001715699,0.0050925124],"category_scores_gemma":[0.00908081,0.00096056284,0.0015279352,0.002147647,0.0009811308,0.0021446366,0.0019636266,0.002299817,0.00149554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018765165,0.00014674343,0.001342562,0.00021098644,0.0002843901,0.000113289585,0.00015270636,0.5041585,0.0034370092,0.07688714,0.0052995067,0.40777946],"study_design_scores_gemma":[0.000023215249,0.000012678928,0.00012623772,0.000011735835,0.000029330822,0.00003556375,0.0000063893162,0.9601061,0.0005566758,0.037549656,0.0015276565,0.000014851469],"about_ca_topic_score_codex":0.009842877,"about_ca_topic_score_gemma":0.0145820435,"teacher_disagreement_score":0.009842877,"about_ca_system_score_codex":0.0011440939,"about_ca_system_score_gemma":0.0019625803,"threshold_uncertainty_score":0.019571185},"labels":[],"label_agreement":null},{"id":"W2045385587","doi":"10.1016/s1476-9271(02)00097-x","title":"Gene teams: a new formalization of gene clusters for comparative genomics","year":2003,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Gene; Comparative genomics; Software; Gene cluster; Computer science; Computational biology; Genetics; Biology; Order (exchange); Genomics; Genome; Programming language","score_opus":0.01356422615412321,"score_gpt":0.26815958746315716,"score_spread":0.25459536130903393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045385587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012401371,0.000080189595,0.9960485,0.00012953929,0.000041072577,0.00003885734,0.00020222261,0.0012925706,0.00092691253],"genre_scores_gemma":[0.041722354,0.00023975392,0.95329714,0.00023351732,0.000092135546,0.00037728605,0.0010346514,0.0015727038,0.0014304222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637324,0.0012781945,0.00034540868,0.0008681462,0.0008709915,0.00026403862],"domain_scores_gemma":[0.99468696,0.0024977142,0.00031012794,0.0016378723,0.00054244976,0.0003248979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004847935,0.0014972812,0.0013080095,0.0026144052,0.0020682013,0.004499531,0.0059875567,0.0016654124,0.0064261206],"category_scores_gemma":[0.012854814,0.0015821861,0.0036393187,0.0036087395,0.0058702254,0.008003986,0.006831039,0.0055360864,0.0020826596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012133268,0.00003615648,0.0007309228,0.00021097664,0.000068097565,0.00012701751,0.00059645995,0.021189922,0.0026342596,0.92770314,0.006481029,0.0401007],"study_design_scores_gemma":[0.00007408552,0.00003984786,0.00020273769,0.00010445362,0.0000827434,0.00014106263,0.00015185133,0.10820061,0.0032971317,0.84296006,0.044702623,0.000042751893],"about_ca_topic_score_codex":0.006459048,"about_ca_topic_score_gemma":0.0076398775,"teacher_disagreement_score":0.006459048,"about_ca_system_score_codex":0.001723979,"about_ca_system_score_gemma":0.002547228,"threshold_uncertainty_score":0.02563858},"labels":[],"label_agreement":null},{"id":"W2046340642","doi":"10.1016/j.compbiolchem.2005.04.006","title":"Structural analysis of inhibition mechanisms of Aurintricarboxylic Acid on SARS-CoV polymerase and other proteins","year":2005,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Agency of Canada","funders":"Innovation and Technology Fund; European Commission","keywords":"Aurintricarboxylic acid; RNA-dependent RNA polymerase; Integrase; Biology; RNA polymerase; Polymerase; Binding site; Molecular biology; Docking (animal); Biochemistry; RNA; Chemistry; Enzyme; DNA; Gene","score_opus":0.010217973376814309,"score_gpt":0.2583981917382876,"score_spread":0.24818021836147328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046340642","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99416864,0.0007139428,0.002763854,0.00014908146,0.000022751305,0.000022778206,0.0001446185,0.000134502,0.0018799003],"genre_scores_gemma":[0.9987029,0.00024720083,0.00052568037,0.000021107631,0.000004207226,0.0000074760615,0.0001254136,0.000009603618,0.00035642544],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992514,0.000011128813,0.0000043819405,0.000013986909,0.000023157792,0.00002221842],"domain_scores_gemma":[0.9998882,0.00003363917,0.000033909186,0.000007604175,0.000016837714,0.000019862431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121380675,0.00045284868,0.00048483524,0.00017755033,0.00030226164,0.00027141868,0.0004060078,0.00022039066,0.0015703234],"category_scores_gemma":[0.0002470712,0.00015348598,0.0003821536,0.00011403014,0.00022676689,0.00019463358,0.00012295718,0.0005153325,0.0002667224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014221627,0.00019889198,0.0026198693,0.00023533411,0.00009189724,0.0005587181,0.0000748907,0.037624616,0.9455235,0.004123259,0.0004663003,0.0070605557],"study_design_scores_gemma":[0.0001728891,0.0004905955,0.005497714,0.000022176875,0.00012700657,0.00027368925,0.00010126445,0.19647174,0.79266745,0.0007410506,0.0033912486,0.000043210406],"about_ca_topic_score_codex":0.0033359001,"about_ca_topic_score_gemma":0.0028759788,"teacher_disagreement_score":0.0033359001,"about_ca_system_score_codex":0.00071607745,"about_ca_system_score_gemma":0.00043316526,"threshold_uncertainty_score":0.006632924},"labels":[],"label_agreement":null},{"id":"W2046482196","doi":"10.1016/j.compbiolchem.2007.03.001","title":"A new protocol of analyzing isotope-coded affinity tag data from high-resolution LC–MS spectrometry","year":2007,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute on Drug Abuse; National Heart, Lung, and Blood Institute; National Institutes of Health; Canadian Institute for Theoretical Astrophysics","keywords":"Tandem; Protocol (science); Constraint (computer-aided design); Chemistry; Isotope; Resolution (logic); Tandem mass spectrometry; Mass spectrometry; Computer science; Biological system; Analytical Chemistry (journal); Chromatography; Artificial intelligence; Mathematics; Physics; Materials science; Nuclear physics","score_opus":0.019112471515484338,"score_gpt":0.3039605601926356,"score_spread":0.2848480886771513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046482196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008628636,0.00037471054,0.98206675,0.00029817745,0.0003156363,0.00067561335,0.0009719661,0.0055993944,0.0010691932],"genre_scores_gemma":[0.022005225,0.00047882172,0.96586865,0.0005125921,0.00009877753,0.0022379046,0.0027691142,0.000770218,0.0052587227],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981741,0.00022684937,0.0001742562,0.00046645696,0.00083656854,0.00012192593],"domain_scores_gemma":[0.99817896,0.00036218757,0.00010630362,0.0007430959,0.00045690127,0.00015260697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016698312,0.0016291917,0.0013869645,0.0018283188,0.0013684533,0.0020613305,0.0021346207,0.0013826005,0.0050042807],"category_scores_gemma":[0.0027160922,0.0014729404,0.0012710795,0.0013707287,0.00095630443,0.0015351189,0.0019122508,0.005005043,0.0055557285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017097914,0.00012101514,0.0002991511,0.00015724366,0.00011072044,0.00014605261,0.000058348018,0.0003736238,0.9578476,0.0021773973,0.003192161,0.035345696],"study_design_scores_gemma":[0.00015563317,0.0002981563,0.0030810128,0.00004110899,0.00014310922,0.00226504,0.00004297549,0.01703064,0.9045589,0.0060035624,0.066061005,0.00031890793],"about_ca_topic_score_codex":0.0010805154,"about_ca_topic_score_gemma":0.0028704447,"teacher_disagreement_score":0.0050042807,"about_ca_system_score_codex":0.0006788917,"about_ca_system_score_gemma":0.0015875296,"threshold_uncertainty_score":0.016740978},"labels":[],"label_agreement":null},{"id":"W2050270988","doi":"10.1016/j.compbiolchem.2014.02.002","title":"An ensemble method for prediction of conformational B-cell epitopes from antigen sequences","year":2014,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Epitope; Computational biology; Linear epitope; Computer science; Antigen; Chemistry; Artificial intelligence; Biology; Genetics","score_opus":0.010471139736496462,"score_gpt":0.2641741060421302,"score_spread":0.25370296630563377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050270988","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095961936,0.00078148086,0.8992819,0.00018182733,0.00012664116,0.00008022464,0.00044290462,0.0016663215,0.001476805],"genre_scores_gemma":[0.67706114,0.000500857,0.31632605,0.00029593264,0.00014154779,0.0003307976,0.0018064167,0.0003042296,0.0032329946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968123,0.00012047075,0.000017459979,0.00005154075,0.00008751187,0.000041795094],"domain_scores_gemma":[0.99904615,0.0005838932,0.000037584356,0.00010460649,0.00017464494,0.000053065607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009397428,0.00066789,0.0012978362,0.0006868463,0.00045871196,0.0005464017,0.0013046445,0.00085197383,0.0017372452],"category_scores_gemma":[0.0020054823,0.0005037407,0.0009816465,0.0006891579,0.00023514456,0.0007638613,0.00082584476,0.0012030952,0.00042441057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002617706,0.00012341911,0.0015490408,0.00006752865,0.00027603918,0.000095490665,0.000031096744,0.81010365,0.004462724,0.0024754158,0.0025749835,0.17797875],"study_design_scores_gemma":[0.0000048019797,0.0000121299245,0.00008578913,0.0000015333127,0.000008208065,0.0000065727304,0.0000011434345,0.9987331,0.00029529317,0.00074672594,0.00010270944,0.0000020248917],"about_ca_topic_score_codex":0.0046732486,"about_ca_topic_score_gemma":0.0060089533,"teacher_disagreement_score":0.0046732486,"about_ca_system_score_codex":0.00035993132,"about_ca_system_score_gemma":0.00083153177,"threshold_uncertainty_score":0.009292066},"labels":[],"label_agreement":null},{"id":"W2064533484","doi":"10.1016/j.compbiolchem.2015.04.002","title":"Characterizing the protonation states of the catalytic residues in apo and substrate-bound human T-cell leukemia virus type 1 protease","year":2015,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"T-cell and Retrovirus Studies","field":"Immunology and Microbiology","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Towson University; McGill University","keywords":"Protonation; Protease; Chemistry; Active site; Substrate (aquarium); Stereochemistry; Catalysis; Enzyme; Biochemistry; Biology; Organic chemistry","score_opus":0.018111304016976765,"score_gpt":0.251542210095669,"score_spread":0.23343090607869224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064533484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988501,0.00005378821,0.00079971575,0.000021452639,0.0000026450548,0.0000026061912,0.000035413505,0.000008503488,0.00022579628],"genre_scores_gemma":[0.99952304,0.000036798032,0.00026140353,0.00000533691,9.556796e-7,0.0000020967716,0.000079759004,0.0000030722824,0.00008756689],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999144,0.000018966486,0.000004108713,0.000019280018,0.000022042948,0.000021242187],"domain_scores_gemma":[0.9998355,0.000086809225,0.000027388513,0.000010507639,0.000018229446,0.000021551044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002367332,0.0002149277,0.00018741016,0.00009810352,0.00017947612,0.0003296558,0.00037404767,0.00027633147,0.0010485396],"category_scores_gemma":[0.00078187033,0.0001404619,0.00012997132,0.00014250074,0.000242237,0.00035292094,0.00012351018,0.0005346523,0.00008003586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030551774,0.00026638267,0.017125478,0.00022150381,0.00016126598,0.0006708061,0.0004197319,0.025506353,0.93616915,0.0031690355,0.0005174007,0.012717814],"study_design_scores_gemma":[0.00030534042,0.00081138156,0.0316812,0.000016232096,0.00009172381,0.0006544365,0.0006217407,0.31452665,0.6480548,0.0020992376,0.0010695293,0.000067746834],"about_ca_topic_score_codex":0.0013900422,"about_ca_topic_score_gemma":0.0012336933,"teacher_disagreement_score":0.0013900422,"about_ca_system_score_codex":0.00033069772,"about_ca_system_score_gemma":0.00017860495,"threshold_uncertainty_score":0.0035076737},"labels":[],"label_agreement":null},{"id":"W2112995167","doi":"10.1016/j.compbiolchem.2014.01.010","title":"Practical halving; the Nelumbo nucifera evidence on early eudicot evolution","year":2014,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Ancestor; Most recent common ancestor; Biology; Genome; Evolutionary biology; Lotus; Gene duplication; Chromosome; Eukaryote; Genetics; Gene; Botany; History","score_opus":0.01922493082392011,"score_gpt":0.2877647416265922,"score_spread":0.2685398108026721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112995167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9489654,0.003599062,0.012092903,0.0034433499,0.00003606767,0.000013549623,0.00054533867,0.00009900375,0.03120537],"genre_scores_gemma":[0.99585164,0.00068033976,0.0021266886,0.00013084218,0.0000061301826,0.000004045086,0.000085260064,0.000017717883,0.0010973437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976915,0.00007329143,0.00001304735,0.00006295201,0.00005032541,0.000031319414],"domain_scores_gemma":[0.99859494,0.000666115,0.00028979467,0.0001896041,0.00016164767,0.00009795996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008476066,0.00031702846,0.00020010256,0.0006508953,0.00093780947,0.0010054283,0.00041677788,0.00044879172,0.00479918],"category_scores_gemma":[0.004357613,0.00013666847,0.00009483157,0.0007905143,0.0017212968,0.0009904866,0.0008496807,0.0004190447,0.000314812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021962752,0.00007901645,0.2556882,0.0015450183,0.00017076678,0.00473275,0.008802451,0.011909525,0.16896799,0.18756469,0.0031759504,0.35516742],"study_design_scores_gemma":[0.00007463729,0.00019967098,0.7690622,0.00058473943,0.00015082253,0.0022825238,0.005221088,0.014999188,0.03391009,0.07724942,0.096174605,0.00009101788],"about_ca_topic_score_codex":0.015815496,"about_ca_topic_score_gemma":0.029490044,"teacher_disagreement_score":0.015815496,"about_ca_system_score_codex":0.0013643489,"about_ca_system_score_gemma":0.00091748306,"threshold_uncertainty_score":0.031446874},"labels":[],"label_agreement":null},{"id":"W2156730759","doi":"10.1016/j.compbiolchem.2006.06.003","title":"A comment on “Prediction of protein structural classes by a new measure of information discrepancy”","year":2006,"lang":"en","type":"letter","venue":"Computational Biology and Chemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Measure (data warehouse); Computer science; Folding (DSP implementation); Protein structure prediction; Homology (biology); Protein folding; Class (philosophy); Computational biology; Protein structure; Artificial intelligence; Data mining; Biology; Genetics; Biochemistry; Amino acid; Engineering","score_opus":0.004579167231730309,"score_gpt":0.22660544648438322,"score_spread":0.22202627925265292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156730759","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031343772,0.0005631743,0.0004696383,0.9669439,0.030862387,0.000011995292,0.00011653583,0.000043681852,0.00067514495],"genre_scores_gemma":[0.0017771325,0.00016310785,0.00033645763,0.97466415,0.021942895,0.00003137521,0.000022498547,0.000024284613,0.0010381078],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98609525,0.0031165294,0.0017459411,0.0029530395,0.0044376375,0.0016515213],"domain_scores_gemma":[0.95474315,0.030465603,0.002589852,0.0016024954,0.007703555,0.0028954085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01327568,0.0019443684,0.0033092522,0.0015644782,0.0068490594,0.0062166094,0.006438557,0.09955067,0.0050384672],"category_scores_gemma":[0.08540477,0.0020048544,0.0033045374,0.0017769244,0.009313487,0.005627271,0.0032091483,0.08426244,0.006981116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009492199,0.000025063806,0.00046974653,0.00004714785,0.000046057117,0.00044824657,0.00011152378,0.000101975806,0.00019304713,0.0033698038,0.9914385,0.0036539128],"study_design_scores_gemma":[0.00092191465,0.00027632658,0.007013912,0.00097246224,0.0004190755,0.002202638,0.0007748703,0.004652575,0.0025843708,0.081926644,0.89780223,0.00045297723],"about_ca_topic_score_codex":0.012376712,"about_ca_topic_score_gemma":0.017645547,"teacher_disagreement_score":0.09955067,"about_ca_system_score_codex":0.0073591974,"about_ca_system_score_gemma":0.0041346652,"threshold_uncertainty_score":0.070209384},"labels":[],"label_agreement":null},{"id":"W2195864334","doi":"10.1016/j.compbiolchem.2015.12.001","title":"Deceptive responsive genes in gel-based proteomics","year":2015,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Proteome; Proteomics; Two-dimensional gel electrophoresis; Gel electrophoresis; Gene; Protein expression; Computational biology; Gene expression; Biology; Chemistry; Chromatography; Molecular biology; Biochemistry","score_opus":0.02010112075595443,"score_gpt":0.30721658191579787,"score_spread":0.28711546115984343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195864334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21647291,0.0037705302,0.76987773,0.0029233021,0.0003013997,0.0000695608,0.00028481954,0.0011348652,0.0051648407],"genre_scores_gemma":[0.83536017,0.0014954215,0.15634722,0.0006674616,0.00013941247,0.00010632202,0.00019712503,0.0001354734,0.005551409],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979405,0.00083589274,0.00006013734,0.00030104766,0.0007528385,0.000109530825],"domain_scores_gemma":[0.99587613,0.0024733336,0.00047566663,0.00074244104,0.00029112963,0.0001413329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021619536,0.0005518476,0.0007649124,0.00058550533,0.00040499924,0.001629225,0.0013911196,0.0018186355,0.0010705594],"category_scores_gemma":[0.006439975,0.00045110416,0.00035987867,0.0006889839,0.0025034714,0.0018955899,0.0015705506,0.0026406958,0.0006198519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012473263,0.00026980916,0.0061857994,0.0007162146,0.000102974256,0.00054893503,0.00045126915,0.20546576,0.41112968,0.20978978,0.002654866,0.16143757],"study_design_scores_gemma":[0.000031429332,0.00017926598,0.0021336775,0.000061595005,0.000030948824,0.00036420507,0.00014480109,0.63542014,0.18655123,0.17044409,0.0045666266,0.00007213159],"about_ca_topic_score_codex":0.0006861279,"about_ca_topic_score_gemma":0.00075421983,"teacher_disagreement_score":0.0021619536,"about_ca_system_score_codex":0.0014834435,"about_ca_system_score_gemma":0.0005410523,"threshold_uncertainty_score":0.011433601},"labels":[],"label_agreement":null},{"id":"W2595628180","doi":"10.1016/j.compbiolchem.2017.03.007","title":"Structural modeling of human organic cation transporters","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Drug Transport and Resistance Mechanisms","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Canadian Institutes of Health Research","keywords":"Major facilitator superfamily; Homology modeling; Protein structure prediction; Protein structure; Protein Data Bank (RCSB PDB); Transmembrane domain; Threading (protein sequence); Ramachandran plot; Solute carrier family; Computational biology; Lactose permease; Transmembrane protein; Loop modeling; Transporter; Docking (animal); Transport protein; Organic cation transport proteins; Chemistry; Biology; Biochemistry; Membrane transport protein; Amino acid","score_opus":0.01835604678689292,"score_gpt":0.2920989465238695,"score_spread":0.2737428997369766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595628180","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89648783,0.0018772667,0.054101076,0.0037169366,0.00012252096,0.00008848239,0.0063019143,0.0013381266,0.035965893],"genre_scores_gemma":[0.9809234,0.0008416355,0.010874914,0.00021548013,0.000022432161,0.000111896494,0.0034335402,0.00011850961,0.003458134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986696,0.000049934468,0.0000053773615,0.000022975033,0.000028381855,0.0000263223],"domain_scores_gemma":[0.99980515,0.000102317106,0.00002229265,0.000016821421,0.000033404678,0.000020028352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020014033,0.0006585292,0.0007878299,0.00033959062,0.0006707211,0.00084217964,0.0009667953,0.0011459116,0.005293013],"category_scores_gemma":[0.00070416904,0.0003492376,0.00074891816,0.0005576677,0.00040291424,0.00048726037,0.0003081867,0.0007051942,0.0008615867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018594059,0.000109027635,0.001903281,0.00012331424,0.00007697661,0.00039357806,0.000119678705,0.96557873,0.0036290938,0.019937692,0.0022748897,0.0056678373],"study_design_scores_gemma":[0.00006669854,0.00004572257,0.00059572567,0.000014426125,0.000029420877,0.00007171723,0.000092564755,0.9854973,0.0012356119,0.009463705,0.002875929,0.000011219649],"about_ca_topic_score_codex":0.019338714,"about_ca_topic_score_gemma":0.016198205,"teacher_disagreement_score":0.019338714,"about_ca_system_score_codex":0.001052731,"about_ca_system_score_gemma":0.0018730859,"threshold_uncertainty_score":0.038452327},"labels":[],"label_agreement":null},{"id":"W2610924493","doi":"10.1016/j.compbiolchem.2017.03.012","title":"PECC: Correcting contigs based on paired-end read distribution","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Computer science","score_opus":0.020562206355106173,"score_gpt":0.275028273531399,"score_spread":0.25446606717629283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610924493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012085792,0.00040621107,0.90614974,0.00017726276,0.0007473892,0.00014020552,0.00324446,0.07519258,0.0018563253],"genre_scores_gemma":[0.055745374,0.00019058569,0.91868585,0.00027377368,0.00021966425,0.00025836704,0.008927322,0.008580954,0.007118055],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966989,0.00047112672,0.00019422286,0.001188326,0.0012023571,0.00024495332],"domain_scores_gemma":[0.9898627,0.0028614951,0.0007068338,0.003931343,0.0023537253,0.00028387326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031096202,0.0033154166,0.0016824107,0.0031924027,0.002119367,0.0017015185,0.0036152368,0.0025058328,0.015080969],"category_scores_gemma":[0.015097977,0.0014400999,0.001800129,0.0033723966,0.001334066,0.0021652058,0.004097646,0.0030031134,0.01172135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019617323,0.00023042179,0.008870045,0.0011314858,0.00049849466,0.0013154075,0.0006808703,0.03046796,0.08753534,0.017567508,0.075456716,0.77428406],"study_design_scores_gemma":[0.00032011673,0.00039286286,0.0060032713,0.00021614331,0.00039009418,0.0019153327,0.00038226915,0.48944998,0.37486455,0.039073985,0.08667573,0.00031567944],"about_ca_topic_score_codex":0.004332843,"about_ca_topic_score_gemma":0.008393179,"teacher_disagreement_score":0.015080969,"about_ca_system_score_codex":0.00077738485,"about_ca_system_score_gemma":0.0026644075,"threshold_uncertainty_score":0.05045086},"labels":[],"label_agreement":null},{"id":"W2611935182","doi":"10.1016/j.compbiolchem.2017.03.014","title":"Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Heart and Stroke Foundation of Canada","keywords":"DNA methylation; Biology; Methylation; Computational biology; Gene; Breast cancer; Genetics; Genome; Cancer; Gene expression","score_opus":0.01421864875448807,"score_gpt":0.3138896898957741,"score_spread":0.299671041141286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611935182","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99124676,0.00058918307,0.0051784976,0.00014848633,0.000006651225,0.000011995657,0.002248744,0.000079654674,0.0004901173],"genre_scores_gemma":[0.9959247,0.00012470136,0.0019850298,0.000024419403,0.000009776283,0.000008435138,0.0016149703,0.000010648166,0.00029725896],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981076,0.000031123636,0.00001588823,0.00008009132,0.000028348206,0.00003377872],"domain_scores_gemma":[0.99926203,0.00040720764,0.00016958006,0.00004770684,0.00005434188,0.000059106816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000368661,0.0002219852,0.00033809367,0.0011821031,0.0002513394,0.00046072624,0.00037776935,0.0002835371,0.0012682943],"category_scores_gemma":[0.0014871017,0.00016327963,0.0006686676,0.00090904866,0.00017959303,0.00022731889,0.0003134431,0.00040378593,0.00022136762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009237665,0.000086006294,0.8891333,0.000165282,0.0007887318,0.00021638774,0.00016074911,0.0076939478,0.04713912,0.00071664166,0.00096805085,0.052007996],"study_design_scores_gemma":[0.000087093824,0.0003097102,0.7781078,0.000046944628,0.0010654115,0.0011699008,0.0006357829,0.18164165,0.026249073,0.007224992,0.003417331,0.00004438664],"about_ca_topic_score_codex":0.0048936536,"about_ca_topic_score_gemma":0.0084429635,"teacher_disagreement_score":0.0048936536,"about_ca_system_score_codex":0.00030029437,"about_ca_system_score_gemma":0.00034502253,"threshold_uncertainty_score":0.009730339},"labels":[],"label_agreement":null},{"id":"W2729589316","doi":"10.1016/j.compbiolchem.2017.10.011","title":"Designing anti-Zika virus peptides derived from predicted human-Zika virus protein-protein interactions","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Regina; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zika virus; Biology; Computational biology; Virology; Virus; Mechanism (biology); Human pathogen; Bioinformatics; Genetics; Gene","score_opus":0.01963104574886333,"score_gpt":0.3110379511058833,"score_spread":0.29140690535701996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729589316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92786926,0.00072476576,0.06583963,0.00016459543,0.00009338043,0.00030247125,0.00044570622,0.0005152565,0.0040450143],"genre_scores_gemma":[0.94615686,0.00033922747,0.051363975,0.0001347227,0.000016861863,0.00010702521,0.00087812956,0.00005570109,0.00094758894],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999118,0.000021606442,0.0000055676205,0.00002183259,0.000022794393,0.00001634278],"domain_scores_gemma":[0.99986255,0.000057420108,0.000026631671,0.0000070891288,0.000022574864,0.00002387191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021140126,0.0005152797,0.00051334617,0.00023543867,0.00027435552,0.0005137192,0.00041089655,0.00031824005,0.002093196],"category_scores_gemma":[0.0005614049,0.00034448697,0.00044697913,0.00019980645,0.00017722187,0.00029476822,0.00030069202,0.0007328396,0.00048405668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020737764,0.00045034595,0.005588117,0.00066168007,0.0001956027,0.0017800652,0.00010276816,0.17224115,0.76020724,0.003454841,0.0013843172,0.05186019],"study_design_scores_gemma":[0.00057751674,0.0017281633,0.0030100006,0.000036242323,0.00026716522,0.00083220645,0.00016343808,0.64806414,0.33719474,0.0025794217,0.0054985215,0.000048528585],"about_ca_topic_score_codex":0.0003433689,"about_ca_topic_score_gemma":0.0007251601,"teacher_disagreement_score":0.002093196,"about_ca_system_score_codex":0.0002956483,"about_ca_system_score_gemma":0.00034413347,"threshold_uncertainty_score":0.0070024133},"labels":[],"label_agreement":null},{"id":"W2754413393","doi":"10.1016/j.compbiolchem.2017.09.003","title":"Identification of effective DNA barcodes for Triticum plants through chloroplast genome-wide analysis","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Intergenic region; Biology; Chloroplast DNA; Genome; DNA barcoding; Gene; Chloroplast; Genetics; Evolutionary biology","score_opus":0.009393103679191613,"score_gpt":0.2748183102417301,"score_spread":0.2654252065625385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754413393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79870445,0.0029763654,0.18682382,0.00061354676,0.000112267735,0.0002779334,0.0071359123,0.0014072983,0.0019484124],"genre_scores_gemma":[0.77838546,0.0015209387,0.2019222,0.00036358967,0.000036786696,0.0002084824,0.015241783,0.0003637069,0.001957008],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990434,0.00011696272,0.000058576446,0.00033434585,0.00034365457,0.000102910904],"domain_scores_gemma":[0.9977302,0.00058539165,0.00065401447,0.00018081345,0.000675057,0.00017448261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013727066,0.0005379766,0.00068060396,0.0017360995,0.0006305411,0.0012791083,0.00075830246,0.00094935636,0.00073985563],"category_scores_gemma":[0.0034507578,0.0005196116,0.0008644851,0.0015398535,0.00053430896,0.00066469685,0.00094738073,0.0021407949,0.00061582506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016384892,0.00007368288,0.0053546014,0.00025665882,0.000110763314,0.00008758699,0.0001640065,0.0010405256,0.96800685,0.001139085,0.0004316708,0.023170682],"study_design_scores_gemma":[0.00012239385,0.00035982783,0.074955344,0.00013867611,0.0007663047,0.00065081625,0.0004357872,0.047130235,0.84751546,0.0032557407,0.024568679,0.00010075307],"about_ca_topic_score_codex":0.002945842,"about_ca_topic_score_gemma":0.0095276,"teacher_disagreement_score":0.002945842,"about_ca_system_score_codex":0.0011730851,"about_ca_system_score_gemma":0.0018202029,"threshold_uncertainty_score":0.008511305},"labels":[],"label_agreement":null},{"id":"W2768260753","doi":"10.1016/j.compbiolchem.2017.11.006","title":"Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Ebola virus; Virtual screening; Ebolavirus; Drug; Drug discovery; Antiviral drug; Virology; Computational biology; VP40; Ligand (biochemistry); Virus; Medicine; Chemistry; Bioinformatics; Pharmacology; Biology; Biochemistry; Receptor","score_opus":0.03798699347091094,"score_gpt":0.3530572401638146,"score_spread":0.3150702466929037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768260753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9192094,0.0009949933,0.059722237,0.00037110734,0.000104297025,0.00034163566,0.0013697993,0.0023039624,0.015582478],"genre_scores_gemma":[0.9604528,0.00022473179,0.035432503,0.000112228976,0.000012487212,0.00012977497,0.0018913966,0.00009309771,0.0016509608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935263,0.0002558503,0.000026325699,0.00008081989,0.00019631526,0.00008807412],"domain_scores_gemma":[0.9994223,0.00027109188,0.000035458124,0.000062208106,0.00015047207,0.00005842512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010916351,0.001196848,0.0015965033,0.001064379,0.00086194725,0.0009320168,0.0016202087,0.0009643146,0.0041832766],"category_scores_gemma":[0.0019904738,0.00039928273,0.0011755958,0.00084548426,0.0003066799,0.00056983106,0.0009775744,0.0006161019,0.00064867607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021511277,0.0012001711,0.00596969,0.00062892505,0.00060408335,0.001172031,0.00010946269,0.8541778,0.027086938,0.008330351,0.008416775,0.090152785],"study_design_scores_gemma":[0.00015892652,0.00033820988,0.00053790427,0.000010004246,0.00006948337,0.00008825303,0.000041415147,0.99114347,0.0050558336,0.0018662732,0.0006741169,0.000016158318],"about_ca_topic_score_codex":0.0039417073,"about_ca_topic_score_gemma":0.0050139036,"teacher_disagreement_score":0.0041832766,"about_ca_system_score_codex":0.00056372077,"about_ca_system_score_gemma":0.0012986442,"threshold_uncertainty_score":0.013994396},"labels":[],"label_agreement":null},{"id":"W2791278855","doi":"10.1016/j.compbiolchem.2018.03.014","title":"An approach for N-linked glycan identification from MS/MS spectra by target-decoy strategy","year":2018,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoy; Glycan; Computer science; Computational biology; Matching (statistics); Ranking (information retrieval); Identification (biology); Tandem mass spectrometry; Data mining; Pattern recognition (psychology); Mass spectrometry; Chemistry; Artificial intelligence; Mathematics; Biology; Chromatography; Glycoprotein; Statistics; Biochemistry","score_opus":0.01473072576496495,"score_gpt":0.30641890826821716,"score_spread":0.29168818250325224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791278855","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037176397,0.00030394824,0.95888895,0.00012753735,0.000044684977,0.0001473649,0.00025995544,0.0021129397,0.0009383382],"genre_scores_gemma":[0.21574435,0.0005497536,0.7772425,0.00033211726,0.000033602973,0.00033135986,0.0015057734,0.00040355657,0.0038570154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996076,0.00006491469,0.00002317666,0.00012631065,0.0001254712,0.0000525823],"domain_scores_gemma":[0.9997137,0.00006848466,0.00002399699,0.00008184223,0.00008264033,0.000029355499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006908838,0.0009211645,0.0010017916,0.0007424265,0.00053079857,0.0008543216,0.0011085428,0.0007541185,0.0011423918],"category_scores_gemma":[0.0007245888,0.00031478528,0.0008602865,0.00061297533,0.00039856078,0.0008045033,0.0012588071,0.0013782108,0.0008215761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005581728,0.0004878018,0.0022327094,0.0005336013,0.00049918017,0.00045896118,0.00019424669,0.019511957,0.70776814,0.017951163,0.004189785,0.24561433],"study_design_scores_gemma":[0.000089763205,0.0003002231,0.0021345837,0.000023559061,0.00017334451,0.000874042,0.00009493333,0.66338533,0.3077619,0.014874053,0.010171169,0.00011710209],"about_ca_topic_score_codex":0.001553261,"about_ca_topic_score_gemma":0.0021369043,"teacher_disagreement_score":0.001553261,"about_ca_system_score_codex":0.00039541646,"about_ca_system_score_gemma":0.0011704882,"threshold_uncertainty_score":0.003821671},"labels":[],"label_agreement":null},{"id":"W2811297572","doi":"10.1016/j.compbiolchem.2018.06.007","title":"A novel feature selection method to predict protein structural class","year":2018,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Science and Technology Major Project of Guangxi; Natural Sciences and Engineering Research Council of Canada; Ministry of Industry and Information Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Feature (linguistics); Pattern recognition (psychology); Benchmark (surveying); Class (philosophy); Artificial intelligence; Data mining; Feature vector; Projection (relational algebra); Set (abstract data type); Machine learning; Algorithm","score_opus":0.004953738647541233,"score_gpt":0.28958905132522583,"score_spread":0.2846353126776846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811297572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109722376,0.0009493208,0.8811873,0.00030859152,0.00031212284,0.00018500947,0.0013023077,0.0043841195,0.0016488426],"genre_scores_gemma":[0.5744406,0.0003738449,0.41113615,0.00038442068,0.00035974386,0.00040172535,0.0052970937,0.00027946458,0.007326966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952805,0.000054693086,0.000038176793,0.00011444833,0.0002050014,0.000059743645],"domain_scores_gemma":[0.9992779,0.0002656043,0.00004784556,0.000057778958,0.00030181848,0.00004896314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063565816,0.0007531066,0.00122708,0.0018419438,0.00049061625,0.0006460574,0.00096623134,0.000664841,0.0017077918],"category_scores_gemma":[0.0011159074,0.00019523135,0.0007392981,0.0015485841,0.00021613647,0.0005874419,0.00056158873,0.00059079233,0.0008275668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000451571,0.0004180346,0.005772128,0.00010059244,0.00018994533,0.00025861405,0.000034039054,0.013751526,0.043830935,0.0008464543,0.014391898,0.9199544],"study_design_scores_gemma":[0.00014518907,0.00030147695,0.010275031,0.00001670776,0.00014796475,0.00054208643,0.000042452284,0.9604774,0.020508107,0.002118221,0.00537357,0.000051788167],"about_ca_topic_score_codex":0.0024594376,"about_ca_topic_score_gemma":0.0033747917,"teacher_disagreement_score":0.0024594376,"about_ca_system_score_codex":0.000257401,"about_ca_system_score_gemma":0.00068632513,"threshold_uncertainty_score":0.005713105},"labels":[],"label_agreement":null},{"id":"W2914043673","doi":"10.1016/j.compbiolchem.2019.01.014","title":"Identification of coenzyme-binding proteins with machine learning algorithms","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Natural Science Foundation of China","keywords":"Coenzyme A; Cofactor; Random forest; Biochemistry; Classifier (UML); Binding site; Machine learning; Protein sequencing; Enzyme; Computational biology; Biology; Gene; Artificial intelligence; Algorithm; Peptide sequence; Computer science","score_opus":0.00934195659620469,"score_gpt":0.27229489029820947,"score_spread":0.2629529337020048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914043673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1186826,0.0024945147,0.8706157,0.0005167646,0.00008167382,0.0001965738,0.00070322177,0.003933901,0.0027749336],"genre_scores_gemma":[0.41717616,0.001195177,0.5768819,0.00019618346,0.000069281814,0.00020000724,0.0017586604,0.00013495584,0.0023876983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999622,0.00010936417,0.000031146974,0.000102951846,0.00009717167,0.00003747352],"domain_scores_gemma":[0.99937505,0.0003468755,0.00006493171,0.00007195577,0.0001167099,0.000024514005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008152921,0.0008452772,0.0011649167,0.0019377439,0.00063738134,0.0012939866,0.0009933988,0.00096101145,0.0018232603],"category_scores_gemma":[0.0023573604,0.00041121882,0.0010303707,0.0013505219,0.0003612367,0.0009080116,0.00061517075,0.00094653445,0.0013146595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093826593,0.00081594015,0.015376363,0.0007111438,0.0005174463,0.0004103987,0.00008760763,0.22272687,0.07938062,0.015107098,0.00696337,0.65696496],"study_design_scores_gemma":[0.000032877033,0.000051694624,0.0012323569,0.000013741531,0.00004994014,0.000089193214,0.000020639049,0.9747269,0.0140782,0.007861162,0.0018305737,0.000012612788],"about_ca_topic_score_codex":0.0018853488,"about_ca_topic_score_gemma":0.0015779509,"teacher_disagreement_score":0.0019377439,"about_ca_system_score_codex":0.00058641174,"about_ca_system_score_gemma":0.0010726199,"threshold_uncertainty_score":0.0060993433},"labels":[],"label_agreement":null},{"id":"W2923682152","doi":"10.1016/j.compbiolchem.2019.03.016","title":"Predicting drug-target interaction network using deep learning model","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; George & Fay Yee Centre for Healthcare Innovation","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society; University of Manitoba; Manitoba Health Research Council","keywords":"Drug target; Deep learning; Computer science; Drug; Artificial intelligence; Drug-drug interaction; Machine learning; Pharmacology; Medicine","score_opus":0.014651415647155028,"score_gpt":0.2977253774374239,"score_spread":0.28307396179026884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923682152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5201793,0.003927762,0.46075338,0.0015399074,0.00017340464,0.00013933798,0.0030309625,0.00197051,0.008285372],"genre_scores_gemma":[0.9713643,0.000750689,0.02237529,0.00017556017,0.00005028612,0.00006008625,0.0015744461,0.000031765187,0.00361761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998299,0.00002938325,0.000008041304,0.000048189053,0.000046713783,0.000037789916],"domain_scores_gemma":[0.9995597,0.00026011845,0.00005567043,0.000027401016,0.00006125365,0.000035835343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003366431,0.00059761043,0.000821524,0.0011632806,0.0002979343,0.0006436424,0.0007677093,0.0009157975,0.0024666872],"category_scores_gemma":[0.0012021798,0.00035542643,0.00077259744,0.0008932086,0.00022490154,0.00082565483,0.00047759953,0.0010092085,0.00044302817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002697358,0.00035855835,0.013795453,0.00018443163,0.00024126492,0.00054895115,0.000023076755,0.88823736,0.005484018,0.006895254,0.0061714444,0.077790454],"study_design_scores_gemma":[0.000006020504,0.000012593676,0.00035675208,0.0000020617513,0.0000144297865,0.00002396659,0.000001722753,0.99698764,0.00037080183,0.0020466717,0.00017549616,0.0000018353658],"about_ca_topic_score_codex":0.009845769,"about_ca_topic_score_gemma":0.0119260065,"teacher_disagreement_score":0.009845769,"about_ca_system_score_codex":0.0007781813,"about_ca_system_score_gemma":0.0010081106,"threshold_uncertainty_score":0.019576907},"labels":[],"label_agreement":null},{"id":"W2936849164","doi":"10.1016/j.compbiolchem.2019.04.005","title":"Oxidatively-mediated in silico epimerization of a highly amyloidogenic segment in the human calcitonin hormone (hCT15-19)","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; Canada Research Chairs; York University; University of Toronto","funders":"European Regional Development Fund; Emberi Eroforrások Minisztériuma; European Commission","keywords":"Chemistry; Epimer; Calcitonin; Peptide; Residue (chemistry); Stereochemistry; Molecular mechanics; Biophysics; Biochemistry; Molecular dynamics; Computational chemistry; Biology","score_opus":0.016445488863414782,"score_gpt":0.3126261157537466,"score_spread":0.2961806268903318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936849164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902886,0.000109343484,0.0063179075,0.00007338419,0.000036228892,0.000016469696,0.00034869357,0.00018938335,0.0026199673],"genre_scores_gemma":[0.9954072,0.00008617446,0.0031491232,0.00003021096,0.000004358844,0.000011275776,0.000543803,0.00004764201,0.0007202753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999044,0.000016994116,0.0000029138791,0.000026980542,0.00002466976,0.000024074354],"domain_scores_gemma":[0.99977547,0.00012663644,0.00002759027,0.000021125707,0.00003431036,0.000014960353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028894783,0.00041180587,0.00028881236,0.00013299602,0.00029101942,0.00034431677,0.0005176244,0.0003907761,0.0037641013],"category_scores_gemma":[0.0006461811,0.00018243723,0.00036890848,0.0001605674,0.0001928548,0.00035324003,0.00030272538,0.00063782977,0.000358593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033950594,0.00084888464,0.008418429,0.0008490182,0.0002938916,0.0014415571,0.0005478206,0.52101463,0.42356145,0.011103482,0.0030228235,0.02550294],"study_design_scores_gemma":[0.00014728538,0.0013876631,0.0026858267,0.000021947706,0.00013564632,0.00013501533,0.00015264133,0.75201064,0.23889953,0.0018042434,0.0025768692,0.000042556803],"about_ca_topic_score_codex":0.002315602,"about_ca_topic_score_gemma":0.0022910207,"teacher_disagreement_score":0.0037641013,"about_ca_system_score_codex":0.00027368878,"about_ca_system_score_gemma":0.00035998985,"threshold_uncertainty_score":0.012592137},"labels":[],"label_agreement":null},{"id":"W2945229001","doi":"10.1016/j.compbiolchem.2019.05.005","title":"Virtual screening of p53 mutants reveals Y220S as an additional rescue drug target for PhiKan083 with higher binding characteristics","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Cancer-related Molecular Pathways","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mutant; Virtual screening; Docking (animal); Drug; Drug discovery; Chemistry; Small molecule; Computational biology; Biology; Biochemistry; Pharmacology; Gene; Medicine","score_opus":0.009613033876857018,"score_gpt":0.26158652586269016,"score_spread":0.25197349198583313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945229001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97674835,0.0027878168,0.0058129695,0.00044116905,0.00013402331,0.00011251161,0.003227925,0.001343415,0.009391897],"genre_scores_gemma":[0.98767614,0.0009906603,0.0046621333,0.00018652552,0.000007319719,0.00006038538,0.0036639054,0.00006539356,0.0026876584],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998926,0.000012993675,0.0000043727846,0.000025057958,0.000042251508,0.000022699547],"domain_scores_gemma":[0.9999646,0.000008755792,0.000005312095,0.0000033440206,0.0000065876347,0.000011399425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017065655,0.0008184758,0.0009109153,0.00034904457,0.00031444157,0.00045039438,0.0005542305,0.00038809516,0.004676404],"category_scores_gemma":[0.00022959229,0.00015795774,0.00064686046,0.00033298245,0.00015310082,0.00022886819,0.0003839314,0.00046709602,0.0009763048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00506873,0.0013418929,0.0090155145,0.0014287182,0.00069438457,0.0024849416,0.00011427298,0.073774256,0.78381926,0.0028205786,0.015098485,0.10433902],"study_design_scores_gemma":[0.0018791775,0.0076465514,0.01658019,0.00015141585,0.001600297,0.0038256207,0.00040172,0.26823956,0.6457762,0.0023465876,0.051326506,0.00022613564],"about_ca_topic_score_codex":0.0014324343,"about_ca_topic_score_gemma":0.0020252706,"teacher_disagreement_score":0.004676404,"about_ca_system_score_codex":0.0003154877,"about_ca_system_score_gemma":0.00038224855,"threshold_uncertainty_score":0.015644133},"labels":[],"label_agreement":null},{"id":"W2974374559","doi":"10.1016/j.compbiolchem.2019.107128","title":"The initial stage of structural transformation of Aβ42 peptides from the human and mole rat in the presence of Fe2+ and Fe3+: Related to Alzheimer's disease","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Chiba University","keywords":"Chemistry; Folding (DSP implementation); Intermolecular force; Protein folding; Amyloid (mycology); Crystallography; Biophysics; Fibril; Beta sheet; Peptide; Stereochemistry; Biochemistry; Molecule; Biology; Organic chemistry; Inorganic chemistry","score_opus":0.019549180638884334,"score_gpt":0.33491720384068974,"score_spread":0.3153680232018054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974374559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973671,0.00016273426,0.0010267241,0.00003172061,0.000007690596,0.00000929022,0.00016013661,0.00002052205,0.0012141716],"genre_scores_gemma":[0.9975068,0.00010840272,0.00097384426,0.000022601092,0.0000015046307,0.000005873472,0.00030543152,0.000010132706,0.0010654356],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999604,0.000004696117,0.000001520549,0.000009610495,0.000009241415,0.0000144055175],"domain_scores_gemma":[0.99995685,0.000009792059,0.000010017181,0.0000042671427,0.000010172706,0.000008979531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006703635,0.00015273032,0.00008936868,0.00008596859,0.00013941483,0.00012752488,0.00016315668,0.00017240776,0.0012107332],"category_scores_gemma":[0.00016218184,0.000097230026,0.0001845611,0.00007582337,0.00010673946,0.00018175556,0.00009579097,0.00031236437,0.00016423556],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048234992,0.00004528746,0.0012004395,0.000052298627,0.000013766919,0.00019534418,0.00009234396,0.00095860026,0.99376076,0.0005329949,0.00012071451,0.002545056],"study_design_scores_gemma":[0.000023699262,0.00041819838,0.018151041,0.000008675823,0.00002309959,0.00043093145,0.00015280783,0.0049277362,0.97240686,0.0005971484,0.0028436093,0.000016184596],"about_ca_topic_score_codex":0.0014681896,"about_ca_topic_score_gemma":0.0013805019,"teacher_disagreement_score":0.0014681896,"about_ca_system_score_codex":0.000114067,"about_ca_system_score_gemma":0.00016177456,"threshold_uncertainty_score":0.004050255},"labels":[],"label_agreement":null},{"id":"W2981162329","doi":"10.1016/j.compbiolchem.2019.107145","title":"PeSA: A software tool for peptide specificity analysis","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motif (music); Computational biology; Peptide; Substrate specificity; Computer science; Protein–protein interaction; Bioinformatics; Biology; Biochemistry; Enzyme","score_opus":0.005345595785304701,"score_gpt":0.24143830126855598,"score_spread":0.2360927054832513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981162329","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009124793,0.0004963042,0.5184804,0.00020980317,0.00019228549,0.00022707274,0.022705497,0.44233683,0.0062270532],"genre_scores_gemma":[0.118405566,0.0010802906,0.69424075,0.0009668843,0.0001395905,0.002373875,0.07288493,0.09541912,0.014488963],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994516,0.00008702485,0.00007601257,0.00014377804,0.00017451782,0.0000670657],"domain_scores_gemma":[0.9988595,0.00068186386,0.00007936867,0.00014319234,0.00017762292,0.00005832738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012748052,0.0028800291,0.0015541569,0.0024073562,0.0009877383,0.0021490946,0.0024107757,0.0012492423,0.043832306],"category_scores_gemma":[0.004073282,0.0016261649,0.0023203858,0.0016176114,0.0005159687,0.0020835428,0.0020288927,0.00231257,0.015781872],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025936572,0.00045798498,0.00818036,0.004655427,0.0014713552,0.0012579234,0.00065783114,0.041811228,0.0583749,0.029710552,0.46341875,0.38740999],"study_design_scores_gemma":[0.0011548504,0.00028013004,0.0036216038,0.00037541712,0.0005508266,0.0014081412,0.00018977249,0.44046262,0.109747514,0.072687805,0.3691643,0.00035708255],"about_ca_topic_score_codex":0.0018429962,"about_ca_topic_score_gemma":0.001958134,"teacher_disagreement_score":0.043832306,"about_ca_system_score_codex":0.0006382378,"about_ca_system_score_gemma":0.0015593197,"threshold_uncertainty_score":0.14663368},"labels":[],"label_agreement":null},{"id":"W3004422088","doi":"10.1016/j.compbiolchem.2020.107231","title":"Immunopeptidome screening to design An immunogenic construct against PRAME positive breast cancer; An in silico study","year":2020,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"In silico; Immune system; Antigen; Biology; Immunotherapy; Computational biology; Cancer vaccine; Cancer immunotherapy; Adjuvant; Cancer research; Epitope; Immunology; Gene; Genetics","score_opus":0.018754974291850846,"score_gpt":0.27603991216442525,"score_spread":0.2572849378725744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004422088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93087035,0.0008198821,0.0609881,0.00031057606,0.0000485279,0.00017162896,0.001112188,0.0005240979,0.0051546693],"genre_scores_gemma":[0.9292646,0.0007798442,0.065282695,0.00013492559,0.0000068760387,0.00008419619,0.0018031406,0.00006617268,0.0025775363],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985874,0.00003482712,0.0000060953616,0.000032909822,0.000045348082,0.000022133414],"domain_scores_gemma":[0.99983954,0.00009741414,0.000018377947,0.000010951088,0.000020311923,0.000013494896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003681498,0.00063767,0.00060168596,0.00022829544,0.00026977042,0.0005262152,0.00047470877,0.0004762845,0.0025882155],"category_scores_gemma":[0.0005469324,0.00020811631,0.00064357516,0.00021489797,0.00017171934,0.00025377213,0.0002364174,0.0007988285,0.0006559013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009319808,0.0009606237,0.0060726427,0.0009419635,0.00017105292,0.0015880399,0.0001258672,0.19708712,0.7459044,0.0051842695,0.0012879211,0.039744075],"study_design_scores_gemma":[0.00014357029,0.0015267653,0.0015289071,0.000049685033,0.00026919687,0.00092702446,0.00013489985,0.36108968,0.6218009,0.0009474383,0.011545115,0.000036814243],"about_ca_topic_score_codex":0.0006026527,"about_ca_topic_score_gemma":0.0010847661,"teacher_disagreement_score":0.0025882155,"about_ca_system_score_codex":0.00041757085,"about_ca_system_score_gemma":0.0005286991,"threshold_uncertainty_score":0.008658409},"labels":[],"label_agreement":null},{"id":"W3026109786","doi":"10.1016/j.compbiolchem.2020.107284","title":"Computing the distribution of the Robinson-Foulds distance","year":2020,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alfred P. Sloan Foundation","keywords":"Phylogenetic tree; Tree (set theory); Novelty; Computer science; Distribution (mathematics); Tree rearrangement; Dynamic programming; Time complexity; Theoretical computer science; Algorithm; Mathematics; Combinatorics; Biology","score_opus":0.00790778456670406,"score_gpt":0.2281445933039605,"score_spread":0.22023680873725643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026109786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06543364,0.00036688126,0.92967725,0.00050245575,0.000052105715,0.000043119526,0.0003019056,0.0009245988,0.0026980655],"genre_scores_gemma":[0.501034,0.00035499415,0.4936686,0.00018747793,0.000105567124,0.00013369214,0.0013515285,0.00034649138,0.0028175893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845517,0.00031806302,0.00009366744,0.00050387316,0.00045454616,0.00017471302],"domain_scores_gemma":[0.99552166,0.0030966352,0.00031452056,0.00036900942,0.0004868765,0.00021122053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017860263,0.00056178175,0.0010031649,0.0021739108,0.000627116,0.0015089996,0.001896557,0.0012058463,0.0034350874],"category_scores_gemma":[0.016943164,0.00038942782,0.00068956456,0.0016591169,0.0013846766,0.0038158135,0.0015301076,0.0013938241,0.001248034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044310934,0.00018330483,0.009046744,0.0002634482,0.000074653675,0.0003605418,0.00036153823,0.4712968,0.013822014,0.20310153,0.008577456,0.2924688],"study_design_scores_gemma":[0.000026631198,0.000059850747,0.00096280454,0.000014877342,0.000007547492,0.00019131356,0.00004937275,0.88569164,0.003863159,0.10683019,0.0022746637,0.000028041131],"about_ca_topic_score_codex":0.002563302,"about_ca_topic_score_gemma":0.0022841177,"teacher_disagreement_score":0.0034350874,"about_ca_system_score_codex":0.00178985,"about_ca_system_score_gemma":0.0015088825,"threshold_uncertainty_score":0.012986362},"labels":[],"label_agreement":null},{"id":"W3027920886","doi":"10.1016/j.compbiolchem.2020.107287","title":"Predicting novel CircRNA-disease associations based on random walk and logistic regression model","year":2020,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Circular RNAs in diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Logistic regression; Computer science; Similarity (geometry); Random walk; Cross-validation; Computational biology; Data mining; Machine learning; Artificial intelligence; Statistics; Mathematics; Biology","score_opus":0.025528495170017262,"score_gpt":0.2849683260937246,"score_spread":0.2594398309237073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027920886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8109702,0.0022810698,0.18003586,0.0013028244,0.00017844209,0.000110537156,0.0029792907,0.0012042447,0.00093762076],"genre_scores_gemma":[0.9792134,0.00042282915,0.015763955,0.00016845066,0.0001358409,0.00006875815,0.0027472107,0.000046603756,0.0014328564],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989926,0.00036519574,0.00007203368,0.0003731241,0.000083260646,0.00011377157],"domain_scores_gemma":[0.9930201,0.005699193,0.0005318297,0.00024749318,0.00028165051,0.00021973868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029743891,0.000995696,0.0013234005,0.0021284078,0.00046535608,0.0012736304,0.0011553526,0.0012463132,0.002885745],"category_scores_gemma":[0.007106562,0.00038087758,0.0017165551,0.0015387352,0.0004107626,0.0009692669,0.00067213725,0.0016128094,0.0011219361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032911452,0.0011700587,0.59816676,0.00039650075,0.002198592,0.0028197733,0.00014711144,0.25977093,0.006861875,0.004624443,0.006810014,0.1137428],"study_design_scores_gemma":[0.00006636363,0.00014301432,0.010900219,0.000013293678,0.00019840372,0.00039083383,0.000020700734,0.9846298,0.00040949503,0.002828635,0.00038008144,0.000019123372],"about_ca_topic_score_codex":0.0034768458,"about_ca_topic_score_gemma":0.003224747,"teacher_disagreement_score":0.0034768458,"about_ca_system_score_codex":0.00032474686,"about_ca_system_score_gemma":0.00069234706,"threshold_uncertainty_score":0.015730262},"labels":[],"label_agreement":null},{"id":"W3037033798","doi":"10.1016/j.compbiolchem.2020.107325","title":"Virtual screening of approved drugs as potential SARS-CoV-2 main protease inhibitors","year":2020,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto Politécnico Nacional; University of California, San Francisco; National Institutes of Health; Comisión de Operación y Fomento de Actividades Académicas, Instituto Politécnico Nacional; National Institute of General Medical Sciences; Swine Innovation Porc","keywords":"Virtual screening; Protease; Repurposing; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Drug discovery; Drug repositioning; Computational biology; Docking (animal); Small molecule; Pharmacology; 2019-20 coronavirus outbreak; Drug; Chemistry; Medicine; Bioinformatics; Infectious disease (medical specialty); Virology; Biology; Disease; Enzyme; Biochemistry","score_opus":0.01849988759980028,"score_gpt":0.28930442270115586,"score_spread":0.2708045351013556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037033798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92366123,0.01237758,0.023625774,0.0007613507,0.00021306402,0.00053306995,0.0065950328,0.001831386,0.03040149],"genre_scores_gemma":[0.9729354,0.003416806,0.015943913,0.00026784273,0.000034160726,0.00014865969,0.0042865383,0.000039968876,0.002926656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969745,0.00012737178,0.000013166557,0.00003632353,0.00008241806,0.000043359338],"domain_scores_gemma":[0.99986553,0.00007143937,0.000016540273,0.000011460489,0.000015728312,0.000019291325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046635992,0.0008214617,0.0010943584,0.0009058569,0.0002870237,0.0010364061,0.0006509827,0.00044719945,0.004454191],"category_scores_gemma":[0.00082239613,0.000249603,0.0009445876,0.00064540934,0.00014988155,0.00030184782,0.000415125,0.00037592297,0.0005618548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01322156,0.0034046988,0.018531928,0.0035622576,0.0027270664,0.002376311,0.00012019502,0.46984917,0.11685633,0.013466047,0.0256392,0.3302453],"study_design_scores_gemma":[0.0025477307,0.0046640565,0.008123518,0.00022026293,0.0028081622,0.0014277197,0.00016502454,0.84386605,0.08633299,0.006101811,0.043653104,0.00008955978],"about_ca_topic_score_codex":0.0009953086,"about_ca_topic_score_gemma":0.0018708388,"teacher_disagreement_score":0.004454191,"about_ca_system_score_codex":0.00035781832,"about_ca_system_score_gemma":0.00073056115,"threshold_uncertainty_score":0.014900744},"labels":[],"label_agreement":null},{"id":"W3095705067","doi":"10.1016/j.compbiolchem.2020.107413","title":"Genetic analysis of SARS-CoV-2 isolates collected from Bangladesh: Insights into the origin, mutational spectrum and possible pathomechanism","year":2020,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Maternal and Child Health Bureau; Alberta Innovates","keywords":"Biology; Missense mutation; Coronavirus; Genetics; Virus; Virology; Genome; Mutation; Lineage (genetic); Gene; Sequence analysis; Genetic analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Disease; Infectious disease (medical specialty); Medicine","score_opus":0.02672915311005664,"score_gpt":0.3116015604441321,"score_spread":0.2848724073340755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095705067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955681,0.00011237356,0.00034755244,0.0001131992,0.0000061134,0.000022379854,0.0024561079,0.000005646497,0.0013684366],"genre_scores_gemma":[0.9971501,0.00016500067,0.00049379666,0.00003950043,0.000002828988,0.000010264395,0.0016637356,0.000003823031,0.00047092998],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977523,0.00003965446,0.000028654687,0.00006094635,0.00004532043,0.000050308536],"domain_scores_gemma":[0.9997346,0.00006894743,0.00007105171,0.000016305201,0.000070146896,0.000038928487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002194678,0.00038438293,0.00025101585,0.0008163941,0.000483702,0.00051056274,0.00022845244,0.00041776834,0.0020916217],"category_scores_gemma":[0.0006604421,0.00014293348,0.0003590798,0.0015173713,0.0002698332,0.00023498746,0.00030101012,0.00035365263,0.0005308244],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062158535,0.00018031706,0.6711523,0.00030237166,0.00013106155,0.002408363,0.0026509964,0.0017059066,0.30594465,0.0005949383,0.00069913786,0.013608523],"study_design_scores_gemma":[0.00003049145,0.00034383748,0.9599986,0.000079070254,0.00013906778,0.0030939858,0.008526673,0.0023973037,0.019402908,0.00044213192,0.0054939943,0.00005200585],"about_ca_topic_score_codex":0.017234465,"about_ca_topic_score_gemma":0.021325564,"teacher_disagreement_score":0.017234465,"about_ca_system_score_codex":0.00053561665,"about_ca_system_score_gemma":0.0006187193,"threshold_uncertainty_score":0.03426832},"labels":[],"label_agreement":null},{"id":"W3164564480","doi":"10.1016/j.compbiolchem.2021.107516","title":"Efficient assembly consensus algorithms for divergent contig sets","year":2021,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Agence Nationale de la Recherche","keywords":"Contig; Computer science; Alphabet; Task (project management); Range (aeronautics); Algorithm; Function (biology); Sequence assembly; Genome; Theoretical computer science; Biology; Genetics","score_opus":0.01568321517228989,"score_gpt":0.27820668083816447,"score_spread":0.2625234656658746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164564480","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012806983,0.00020261,0.9806293,0.00011085733,0.00006860018,0.00013635583,0.00044812396,0.0046440293,0.00095322746],"genre_scores_gemma":[0.05553697,0.00011399304,0.936658,0.000082884886,0.000040610987,0.0003134957,0.004068551,0.0014775028,0.0017079017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640864,0.000872108,0.0003071883,0.00105969,0.0010122217,0.0003401173],"domain_scores_gemma":[0.9886193,0.0057187076,0.00047358958,0.0018931085,0.0029037062,0.00039166218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00463479,0.0027994476,0.0030498279,0.003402614,0.0037856305,0.0029973714,0.005648011,0.0027980583,0.009179189],"category_scores_gemma":[0.01883461,0.0026462774,0.0030870698,0.0045911516,0.0012245808,0.0030561439,0.0048063784,0.0050498685,0.005809815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011735949,0.00035817482,0.0022202716,0.00081460446,0.0004095065,0.00055662985,0.0010502073,0.2887116,0.034847323,0.03651106,0.01661417,0.61673284],"study_design_scores_gemma":[0.00020954438,0.000119085256,0.0005049967,0.00006269353,0.00009563369,0.00018091699,0.00026971364,0.9181221,0.014791689,0.060436085,0.0051440294,0.00006353152],"about_ca_topic_score_codex":0.006290838,"about_ca_topic_score_gemma":0.011489607,"teacher_disagreement_score":0.009179189,"about_ca_system_score_codex":0.0019229605,"about_ca_system_score_gemma":0.0044612098,"threshold_uncertainty_score":0.030707419},"labels":[],"label_agreement":null},{"id":"W3194842705","doi":"10.1016/j.compbiolchem.2021.107564","title":"Mathematical modeling of the role of bone turnover in pH regulation in bone interstitial fluid","year":2021,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Shriners Hospitals for Children - Canada; Montreal Children's Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Interstitial fluid; Dissolution; Chemistry; Simulated body fluid; Precipitation; Phosphate; Carbonate; Bone remodeling; Biophysics; Mineralogy; Biochemistry; Apatite; Endocrinology; Organic chemistry","score_opus":0.014826414367530925,"score_gpt":0.3062866928096816,"score_spread":0.2914602784421507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194842705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16637945,0.0029562337,0.7787867,0.009260137,0.0006555549,0.00014501884,0.0009837727,0.00032357077,0.04050942],"genre_scores_gemma":[0.9576163,0.0012547278,0.021714257,0.00075375015,0.00028880985,0.0002461855,0.00024747546,0.00013422838,0.017744316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995946,0.00014108214,0.000020090663,0.0000864099,0.00008973399,0.000068035486],"domain_scores_gemma":[0.9986732,0.0007577478,0.00017219207,0.000062382634,0.00020122714,0.00013330385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088716106,0.00068317674,0.0012025182,0.0007160215,0.0007298552,0.001914409,0.0019762593,0.0032037043,0.002386703],"category_scores_gemma":[0.003979587,0.00086498196,0.0011166316,0.0005594389,0.0021649688,0.0020299773,0.0016024824,0.0014546502,0.00029629195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033601336,0.000041705585,0.0004872537,0.00005803858,0.000023484528,0.00007445312,0.000060014874,0.9249826,0.0013443342,0.07066019,0.000685787,0.0015485017],"study_design_scores_gemma":[0.000008776708,0.000004999272,0.00010168189,0.0000035970031,0.000003891239,0.000010658432,0.000008046082,0.9935289,0.00008959655,0.0059880633,0.0002452218,0.000006539379],"about_ca_topic_score_codex":0.014481787,"about_ca_topic_score_gemma":0.0053323014,"teacher_disagreement_score":0.014481787,"about_ca_system_score_codex":0.0014279282,"about_ca_system_score_gemma":0.0021372968,"threshold_uncertainty_score":0.028795004},"labels":[],"label_agreement":null},{"id":"W3196563587","doi":"10.1016/j.compbiolchem.2021.107570","title":"Potential Achilles heels of SARS-CoV-2 are best displayed by the base order-dependent component of RNA folding energy","year":2021,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"RNA; Folding (DSP implementation); Nucleic acid; Base pair; Component (thermodynamics); Biology; Virology; Computational biology; Chemistry; Genetics; DNA; Gene; Physics","score_opus":0.011780949002877435,"score_gpt":0.2521597741695618,"score_spread":0.24037882516668438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196563587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93764615,0.0021580977,0.04504285,0.0015180988,0.00019267338,0.000023977022,0.00036826934,0.00057074387,0.012479159],"genre_scores_gemma":[0.99403304,0.0005242925,0.0036170597,0.00012601705,0.000030347212,0.000011068017,0.00015752179,0.00005325832,0.0014473378],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998596,0.000024193552,0.000006734999,0.000026275178,0.00004306978,0.000040138137],"domain_scores_gemma":[0.9997979,0.00009065532,0.000034007706,0.00002751571,0.000023756247,0.000026292157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032488405,0.0008190064,0.00064984564,0.00030157084,0.00047394374,0.0012871431,0.0005263609,0.0008516086,0.005485603],"category_scores_gemma":[0.0008813975,0.00044666798,0.00033863884,0.00021237867,0.00061665673,0.0015907836,0.0005449579,0.0011750008,0.000904155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014059935,0.00016265726,0.01196394,0.00045913665,0.00020774028,0.0017659691,0.0001980822,0.0663242,0.79570735,0.076013714,0.0013655073,0.044425726],"study_design_scores_gemma":[0.000117156866,0.0013802879,0.020149065,0.000119546894,0.0001614318,0.0016684117,0.00091453415,0.43514225,0.36428043,0.16553612,0.010337192,0.00019355492],"about_ca_topic_score_codex":0.00063631,"about_ca_topic_score_gemma":0.0013134786,"teacher_disagreement_score":0.005485603,"about_ca_system_score_codex":0.00036646167,"about_ca_system_score_gemma":0.0003224402,"threshold_uncertainty_score":0.018351138},"labels":[],"label_agreement":null},{"id":"W4220928358","doi":"10.1016/j.compbiolchem.2022.107668","title":"Glycosylation is key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2","year":2022,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Glycosylation; Glycoprotein; Computational biology; Biology; Drug discovery; Protein domain; Drug development; Drug; Receptor; Cell biology; Chemistry; Bioinformatics; Biochemistry; Gene; Pharmacology","score_opus":0.0363197559364166,"score_gpt":0.3498350507389618,"score_spread":0.3135152948025452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220928358","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96488804,0.00119964,0.026027536,0.0006861971,0.00015167487,0.00006801458,0.00018121574,0.00030110177,0.006496638],"genre_scores_gemma":[0.9890104,0.0005646492,0.008725168,0.00016165955,0.000014907547,0.000018293558,0.00018302396,0.000026277687,0.00129568],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998996,0.000019339694,0.000004070571,0.00002304913,0.000025477126,0.0000284319],"domain_scores_gemma":[0.9999391,0.000014440285,0.000013655342,0.000007235745,0.000014734898,0.000010731067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018961198,0.0003328314,0.0003670277,0.00007170686,0.00020458356,0.0004616635,0.00020911945,0.0002674726,0.0017077837],"category_scores_gemma":[0.00037173295,0.000103102124,0.00032301154,0.00012581721,0.00016635843,0.0005275767,0.00033864012,0.0005673214,0.00036241952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073013606,0.00027772872,0.0048819133,0.000352061,0.00007918779,0.0002709458,0.00010647886,0.02259421,0.9108334,0.0107246265,0.0022309723,0.046918448],"study_design_scores_gemma":[0.000091617636,0.00067739084,0.004546692,0.000028987788,0.00011937027,0.0003305566,0.00015486279,0.1963637,0.7808662,0.0070845666,0.009693589,0.000042561733],"about_ca_topic_score_codex":0.0007380842,"about_ca_topic_score_gemma":0.0007061259,"teacher_disagreement_score":0.0017077837,"about_ca_system_score_codex":0.00024255938,"about_ca_system_score_gemma":0.0003272556,"threshold_uncertainty_score":0.005713105},"labels":[],"label_agreement":null},{"id":"W4221138137","doi":"10.1016/j.compbiolchem.2022.107664","title":"Transfer learning with molecular graph convolutional networks for accurate modeling and representation of bioactivities of ligands targeting GPCRs without sufficient data","year":2022,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Research of Jiangsu Higher Education Institutions of China; National Natural Science Foundation of China","keywords":"Graph; Representation (politics); G protein-coupled receptor; Computer science; Drug discovery; Transfer of learning; Ligand (biochemistry); Convolutional neural network; Artificial intelligence; Computational biology; Machine learning; Theoretical computer science; Chemistry; Bioinformatics; Receptor; Biology; Biochemistry","score_opus":0.029910838045049674,"score_gpt":0.3056978403825742,"score_spread":0.2757870023375245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221138137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07253451,0.00068847695,0.92102545,0.0006012266,0.00007355684,0.00005702144,0.0005009498,0.002896952,0.0016217328],"genre_scores_gemma":[0.8535374,0.0006071163,0.13940047,0.0002685614,0.000060241357,0.0001686735,0.0011816054,0.00022234816,0.004553553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.00004215159,0.000007326664,0.000037576523,0.000037988655,0.000025898058],"domain_scores_gemma":[0.9995431,0.0002760621,0.000038617185,0.00006496388,0.00005489107,0.000022371389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005132814,0.0008895598,0.00070009904,0.00058591843,0.0002929642,0.00065188645,0.0012852014,0.000980166,0.0013639644],"category_scores_gemma":[0.0020527195,0.000513128,0.0008154072,0.00068364665,0.0005440927,0.0011843961,0.0008748615,0.00172446,0.00051049603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007846485,0.000055155717,0.00033726898,0.000042572938,0.00005371714,0.00004445809,0.000017311395,0.93433654,0.0026557522,0.007024364,0.0018023499,0.05355197],"study_design_scores_gemma":[0.0000011893778,0.0000030626418,0.000016563132,5.9289357e-7,0.0000019991026,0.0000016580574,6.789459e-7,0.99818414,0.00029714027,0.0014198119,0.00007227625,8.7620896e-7],"about_ca_topic_score_codex":0.020194968,"about_ca_topic_score_gemma":0.019235343,"teacher_disagreement_score":0.020194968,"about_ca_system_score_codex":0.0013543649,"about_ca_system_score_gemma":0.0012123822,"threshold_uncertainty_score":0.040154874},"labels":[],"label_agreement":null},{"id":"W4224468197","doi":"10.1016/j.compbiolchem.2022.107679","title":"Multiscale modeling of the cellular uptake of C6 peptide-siRNA complexes","year":2022,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Peradeniya; University of Waterloo","keywords":"Peptide; Cell-penetrating peptide; Small interfering RNA; Chemistry; Biophysics; Molecular dynamics; Peptide sequence; Biochemistry; RNA; Biology; Gene","score_opus":0.012267836400701726,"score_gpt":0.2383026481849462,"score_spread":0.22603481178424448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224468197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83275366,0.0011562029,0.12813528,0.002482668,0.00018925592,0.000084083425,0.0008923965,0.0003810593,0.033925522],"genre_scores_gemma":[0.990157,0.00027446917,0.0059540914,0.00020047053,0.00003558393,0.00009586382,0.00019815727,0.00007612422,0.0030082343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987066,0.000027318569,0.000005421909,0.000022824628,0.00003183944,0.00004198448],"domain_scores_gemma":[0.99956304,0.00023982885,0.00004871954,0.000019780711,0.00006191098,0.00006661535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002620706,0.00049967197,0.00083960715,0.00035765942,0.00050125836,0.0009661419,0.000993256,0.0019596852,0.002406565],"category_scores_gemma":[0.001037914,0.00042475015,0.00089392974,0.00038137136,0.00072207797,0.00060525717,0.00059213035,0.00070696283,0.0002630632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004152467,0.00003767381,0.0004851828,0.00003660501,0.000025690324,0.000095447955,0.000028753804,0.98649174,0.003921454,0.00779817,0.00029125964,0.0007463759],"study_design_scores_gemma":[0.0000051853604,0.0000051836987,0.00010973342,0.0000013811963,0.000002758795,0.000005246409,0.000005405746,0.9991048,0.00016399485,0.00051750627,0.00007527905,0.00000348621],"about_ca_topic_score_codex":0.030229114,"about_ca_topic_score_gemma":0.010287374,"teacher_disagreement_score":0.030229114,"about_ca_system_score_codex":0.0015897009,"about_ca_system_score_gemma":0.0013321476,"threshold_uncertainty_score":0.060106337},"labels":[],"label_agreement":null},{"id":"W4291033104","doi":"10.1016/j.compbiolchem.2022.107753","title":"PeSA 2.0: A software tool for peptide specificity analysis implementing positive and negative motifs and motif-based peptide scoring","year":2022,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Peptide; Computational biology; Sequence motif; Motif (music); Computer science; Bioinformatics; Biology; Biochemistry; DNA","score_opus":0.006541357027320561,"score_gpt":0.2623197141176653,"score_spread":0.25577835709034474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291033104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024918694,0.000913637,0.55637807,0.00026630657,0.00026450428,0.00039497792,0.035435803,0.3730768,0.008351187],"genre_scores_gemma":[0.10967273,0.0009663514,0.74592257,0.00061998016,0.00010609948,0.0020484482,0.06852211,0.05856051,0.0135812005],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993782,0.00011081873,0.00007447443,0.00013580488,0.00022633726,0.000074304866],"domain_scores_gemma":[0.9991659,0.00046014425,0.000102222446,0.00008142309,0.0001354899,0.000054773314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020946108,0.0021424661,0.0016471039,0.0026456674,0.0008385849,0.0018709877,0.002401323,0.0010641519,0.036596313],"category_scores_gemma":[0.0035382123,0.0015029695,0.0016351653,0.001444998,0.00048697763,0.0016268883,0.0020137622,0.002041756,0.01584879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003860545,0.0005184757,0.016260974,0.0056116222,0.0015913082,0.001331719,0.00069870014,0.022028131,0.122056395,0.022111908,0.4400585,0.3638717],"study_design_scores_gemma":[0.0011184987,0.0005805823,0.011025957,0.0005270144,0.0006711322,0.0033281313,0.000246497,0.38442484,0.16239987,0.052828588,0.38223633,0.00061259867],"about_ca_topic_score_codex":0.0011096236,"about_ca_topic_score_gemma":0.0015779802,"teacher_disagreement_score":0.036596313,"about_ca_system_score_codex":0.00042218177,"about_ca_system_score_gemma":0.0014164445,"threshold_uncertainty_score":0.12242693},"labels":[],"label_agreement":null},{"id":"W4321768931","doi":"10.1016/j.compbiolchem.2023.107837","title":"Predicting DNA kinetics with a truncated continuous-time Markov chain method","year":2023,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"DNA and Nucleic Acid Chemistry","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia Hospital; Canadian Institute for Advanced Research; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Markov chain; Markov model; Continuous-time Markov chain; Computer science; State space; Truncation (statistics); Markov process; Stochastic matrix; Markov chain Monte Carlo; Mathematics; Algorithm; Statistical physics; Biological system; Balance equation; Mathematical optimization; Statistics; Bayesian probability; Physics","score_opus":0.004300811604689538,"score_gpt":0.24200160352622482,"score_spread":0.2377007919215353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321768931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07587249,0.00026311385,0.9194316,0.00035208085,0.000116439354,0.0000818876,0.00033580462,0.0009626397,0.0025838513],"genre_scores_gemma":[0.83677965,0.00030772405,0.15757538,0.00021714592,0.00011264276,0.00033797038,0.00068701524,0.00022877773,0.0037537955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.0001532648,0.000023233766,0.00009084814,0.000108702785,0.00007302318],"domain_scores_gemma":[0.9904745,0.007918198,0.00027542183,0.0003849352,0.0006005076,0.00034639097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001750791,0.0006289662,0.0015251879,0.00088180735,0.0007469516,0.0010762562,0.0025190029,0.0021054377,0.003932259],"category_scores_gemma":[0.0072357673,0.0011320123,0.0011183386,0.0007602253,0.0011774449,0.0014213637,0.0009271265,0.002045834,0.0006578374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038115428,0.000017943308,0.00021480667,0.00001579537,0.0000107239475,0.000024476434,0.000009455604,0.9939114,0.00024279577,0.003088382,0.00013417001,0.0022919355],"study_design_scores_gemma":[0.0000030510346,0.0000017147689,0.000009428064,7.320318e-7,0.0000010656792,9.801444e-7,4.4061298e-7,0.9993113,0.00005212542,0.0005994809,0.000018681887,9.744507e-7],"about_ca_topic_score_codex":0.0220839,"about_ca_topic_score_gemma":0.013083667,"teacher_disagreement_score":0.0220839,"about_ca_system_score_codex":0.0015054919,"about_ca_system_score_gemma":0.0032524867,"threshold_uncertainty_score":0.04391074},"labels":[],"label_agreement":null},{"id":"W4403681199","doi":"10.1016/j.compbiolchem.2024.108257","title":"CopyMix: Mixture model based single-cell clustering and copy number profiling using variational inference","year":2024,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Profiling (computer programming); Cluster analysis; Inference; Computer science; Mixture model; Artificial intelligence; Pattern recognition (psychology); Data mining; Computational biology; Biology; Programming language","score_opus":0.013807393168974088,"score_gpt":0.27688381401478873,"score_spread":0.2630764208458146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403681199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038797227,0.00008464416,0.9949734,0.00010481107,0.000018438534,0.000034612036,0.0001077894,0.0005761229,0.00022044993],"genre_scores_gemma":[0.18650506,0.0002799607,0.8053778,0.00031098272,0.00011248322,0.00044760853,0.0015913346,0.001190665,0.0041841697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988072,0.0005391081,0.000037857724,0.0003098437,0.00023434311,0.000071621806],"domain_scores_gemma":[0.9974687,0.0018188948,0.00018127084,0.0002423563,0.00017694644,0.00011188058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037242644,0.0012656866,0.00152256,0.0011627798,0.00082366826,0.0016315869,0.0034434046,0.0020356548,0.002708132],"category_scores_gemma":[0.007415519,0.0013487956,0.002541666,0.0010767663,0.0013805822,0.0017734187,0.0025448485,0.002820316,0.0007293768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022211784,0.000069475085,0.002304956,0.0001292317,0.00033022414,0.00012584642,0.00015187,0.8765096,0.0061629075,0.041521605,0.0029482916,0.06952382],"study_design_scores_gemma":[0.000008133319,0.0000074966747,0.00009377303,0.0000032803655,0.000007860367,0.000015938487,0.000003223674,0.9904351,0.0006673912,0.008259467,0.00048887,0.000009493881],"about_ca_topic_score_codex":0.011686735,"about_ca_topic_score_gemma":0.011059887,"teacher_disagreement_score":0.011686735,"about_ca_system_score_codex":0.0017815569,"about_ca_system_score_gemma":0.0020925147,"threshold_uncertainty_score":0.023237407},"labels":[],"label_agreement":null},{"id":"W4406821406","doi":"10.1016/j.compbiolchem.2025.108363","title":"NAVT-net neuron attention visual taylor network for lung cancer detection using CT images","year":2025,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Lung cancer; Computer science; Artificial intelligence; Visual attention; Pattern recognition (psychology); Computer vision; Radiology; Neuroscience; Medicine; Pathology; Psychology","score_opus":0.0069451269747038994,"score_gpt":0.3432960146900033,"score_spread":0.3363508877152994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406821406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29713213,0.004874706,0.6648449,0.0023532407,0.0014375992,0.00026217135,0.0016617442,0.0064701107,0.02096333],"genre_scores_gemma":[0.94146144,0.0005966885,0.043979328,0.00037539835,0.00014158287,0.00006531955,0.00062933913,0.000080818725,0.012669973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989915,0.00001204819,0.000004027944,0.00003135287,0.000022420309,0.000030960306],"domain_scores_gemma":[0.9998242,0.000044824625,0.000010776434,0.000013624664,0.00008496117,0.000021673332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003585506,0.00060901494,0.0004484323,0.0004573857,0.00029671003,0.00043694227,0.0009916136,0.000804176,0.0041450863],"category_scores_gemma":[0.0009851285,0.00020363035,0.0004843553,0.00041774186,0.00017453884,0.0005247894,0.00061514956,0.0006732065,0.0006207092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005943452,0.00028268585,0.003321444,0.0001749591,0.00014286135,0.00024068443,0.000051558993,0.19959772,0.02517195,0.0028660772,0.016188176,0.7513676],"study_design_scores_gemma":[0.00001021923,0.00006394162,0.0006368362,0.000007685792,0.000025593594,0.000037272923,0.000010213122,0.9914209,0.00586728,0.0012781814,0.0006354924,0.0000063428265],"about_ca_topic_score_codex":0.019639779,"about_ca_topic_score_gemma":0.025210854,"teacher_disagreement_score":0.019639779,"about_ca_system_score_codex":0.0008077271,"about_ca_system_score_gemma":0.0008680252,"threshold_uncertainty_score":0.039050937},"labels":[],"label_agreement":null},{"id":"W4417248747","doi":"10.1016/j.compbiolchem.2025.108838","title":"Valosin-Containing Protein as a therapeutic target in CAG repeat–driven Spinocerebellar ataxias: Integrative transcriptomic and computational insights","year":2025,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Genetic Neurodegenerative Diseases","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Council of Scientific and Industrial Research, India; Mount Royal University","keywords":"Transcriptome; Gene; Spinocerebellar ataxia; Virtual screening; Regulator; Drug discovery; HEK 293 cells; Downregulation and upregulation","score_opus":0.013243729584026651,"score_gpt":0.2755838150336867,"score_spread":0.26234008544966003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417248747","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9800608,0.0012283499,0.015782224,0.0003531785,0.000029850506,0.000029799196,0.0005373529,0.0002230347,0.0017554268],"genre_scores_gemma":[0.99007857,0.00051691406,0.008580006,0.000038573344,0.0000042760307,0.000017499737,0.00034487253,0.000021571444,0.00039772547],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999579,0.000010493602,0.0000015403612,0.000009989573,0.000013972502,0.0000061241103],"domain_scores_gemma":[0.99996233,0.000016072248,0.00000842391,0.0000020285522,0.0000049646264,0.0000061494557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001616982,0.00020813994,0.0003327403,0.00011283018,0.00014840016,0.0004177456,0.00019111851,0.00029372267,0.00041050228],"category_scores_gemma":[0.00017529688,0.000074866424,0.00019789417,0.0001340859,0.00014668787,0.00016482951,0.00011267193,0.0003442565,0.0000875247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001290084,0.00026714825,0.0032591815,0.00028100872,0.00008551129,0.00034295794,0.0000682076,0.11548875,0.84122413,0.007207561,0.00078480644,0.029700626],"study_design_scores_gemma":[0.00010042032,0.00059297035,0.0044050245,0.00003183073,0.00014798719,0.00014531627,0.00011103703,0.7830394,0.2046112,0.0036238323,0.0031590594,0.000031944037],"about_ca_topic_score_codex":0.0013247662,"about_ca_topic_score_gemma":0.0020490629,"teacher_disagreement_score":0.0013247662,"about_ca_system_score_codex":0.0003997573,"about_ca_system_score_gemma":0.00036833744,"threshold_uncertainty_score":0.0029004216},"labels":[],"label_agreement":null},{"id":"W609430750","doi":"10.1016/j.compbiolchem.2015.06.002","title":"The functional landscape bound to the transcription factors of Escherichia coli K-12","year":2015,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Regulon; Gene; Escherichia coli; Biology; DNA; Genetics; Transcription (linguistics); Transcription factor; Computational biology; DNA-binding domain; DNA sequencing; Transcriptional regulation","score_opus":0.014630749189373855,"score_gpt":0.22997667172208358,"score_spread":0.21534592253270973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W609430750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9864259,0.00017066386,0.0038741843,0.00066014216,0.000010825436,0.000005446508,0.00040178763,0.00012602723,0.008325082],"genre_scores_gemma":[0.99772507,0.00007513718,0.0008850407,0.00004208339,0.0000036844792,0.0000096145495,0.00038441241,0.00003744699,0.0008375893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.00003669556,0.000004567196,0.000022260996,0.000034319466,0.0000531346],"domain_scores_gemma":[0.9996884,0.00014918965,0.000022010576,0.000022847687,0.000055134155,0.00006242039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022051194,0.00032671972,0.0005175057,0.0005306028,0.0009179977,0.0014719295,0.00050520775,0.00071466184,0.0070306123],"category_scores_gemma":[0.0012107608,0.00030468823,0.00024339664,0.00045439368,0.000811434,0.0007503143,0.00042063073,0.00048436984,0.0004774905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015576753,0.00032904363,0.022117162,0.00034181154,0.00017984628,0.0008924999,0.00038564712,0.7234523,0.12180654,0.10404488,0.0065515162,0.018341012],"study_design_scores_gemma":[0.00006570523,0.0001716067,0.017748319,0.000021963875,0.00004934361,0.00011707728,0.00045155507,0.935483,0.010369507,0.033588696,0.0018904599,0.000042813022],"about_ca_topic_score_codex":0.0057269493,"about_ca_topic_score_gemma":0.0056854244,"teacher_disagreement_score":0.0070306123,"about_ca_system_score_codex":0.00090080965,"about_ca_system_score_gemma":0.00067957403,"threshold_uncertainty_score":0.023519695},"labels":[],"label_agreement":null}]}