{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":200,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":200,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"6c193b2d3746","filters":{"venue":"Briefings in Bioinformatics"}},"results":[{"id":"W2168465568","doi":"10.1093/bib/bbp043","title":"Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology","year":2009,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":707,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences","keywords":"Computer science; Systems biology; SBML; Software; Organism; Metabolic pathway; Model organism; Visualization; Metabolic network; Reachability; Biological network; Tracing; Computational biology; World Wide Web; Biology; Data mining; Gene; Genetics; XML; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.009029125518549033,"gpt":0.2114062942786935,"spread":0.2023771687601444,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003385803,0.000202857,0.0002392228,0.0001181187,0.00007583468,0.00007961106,0.0001402164,0.0002618145,0.000001575697],"category_scores_gemma":[0.0003522334,0.0001811655,0.00004706321,0.0001782401,0.00004842174,0.00002912109,0.00005091807,0.0001140792,0.00000467489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002671775,"about_ca_system_score_gemma":0.00007628457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002254541,"about_ca_topic_score_gemma":0.000003365832,"domain_scores_codex":[0.9989351,0.00001468572,0.0005360187,0.0001449605,0.00006804146,0.0003012105],"domain_scores_gemma":[0.9993798,0.00001165397,0.0001702912,0.0002340212,0.0001383128,0.00006598183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002459266,0.0001266773,0.0003452129,0.0008684446,0.00007308679,0.000001198049,0.00142597,0.00280732,0.8300017,0.001535727,0.004860864,0.1577079],"study_design_scores_gemma":[0.003042778,0.00156647,0.001926827,0.0002628673,0.00003489381,0.0001635401,0.001161752,0.01630914,0.08621392,0.0001743134,0.8880384,0.001105128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539313,0.001381897,0.1419823,0.0002349323,0.000585347,0.001005425,0.0004482098,0.0001077898,0.0003227871],"genre_scores_gemma":[0.896377,0.001103547,0.09683632,0.001655088,0.0004446338,0.00005288826,0.003149431,0.00004169071,0.0003393956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8831775,"threshold_uncertainty_score":0.7387714,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2567080747","doi":"10.1093/bib/bbw113","title":"A review on machine learning principles for multi-view biological data integration","year":2016,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":437,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan; University of Windsor; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council Canada; University of Windsor; University of Ottawa","keywords":"Computer science; Artificial intelligence; Machine learning; Deep learning; Tree (set theory); Data integration; Cluster analysis; Biological data; Similarity (geometry); Key (lock); Data mining; Bioinformatics","retraction":null,"screen_n_in":null,"score":{"opus":0.2476449868077063,"gpt":0.4070167608868869,"spread":0.1593717740791806,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008296155,0.0003775332,0.0008125309,0.0001117295,0.0000776233,0.00003863775,0.0007543943,0.0004057105,0.00001476901],"category_scores_gemma":[0.001457429,0.0002327582,0.000212615,0.0001629292,0.00005471919,0.00001212962,0.0003524467,0.0002575975,0.00003815944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005113851,"about_ca_system_score_gemma":0.0001885573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003162227,"about_ca_topic_score_gemma":0.00000745025,"domain_scores_codex":[0.9979742,0.0001268164,0.001054733,0.000464453,0.0001218024,0.0002579505],"domain_scores_gemma":[0.9980924,0.00006507483,0.0007903501,0.0009161418,0.00007137783,0.00006470372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009208486,0.0000356861,0.000001131252,0.01400998,0.00002323621,1.678077e-7,0.000007735218,4.925391e-7,0.00001537608,0.0001384966,0.00780587,0.9779526],"study_design_scores_gemma":[0.0002809468,0.0001206324,0.000001064334,0.05410083,0.00005550876,0.00001069698,0.000003854244,0.0008929198,0.00002389853,0.000004492881,0.9442062,0.0002989803],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.389197e-7,0.9751614,0.02266774,0.0001531209,0.00011185,0.001338279,0.0002870271,0.00002254148,0.000257851],"genre_scores_gemma":[7.032559e-7,0.9796484,0.0109346,0.001320933,0.0001066462,0.0003341302,0.007213907,0.00003462119,0.0004060811],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9776536,"threshold_uncertainty_score":0.9491602,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2585681007","doi":"10.1093/bib/bbw145","title":"Methodological implementation of mixed linear models in multi-locus genome-wide association studies","year":2016,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":418,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Huazhong Agricultural University; National Natural Science Foundation of China","keywords":"Locus (genetics); Genome-wide association study; Bayes' theorem; Bonferroni correction; Quantitative trait locus; Single-nucleotide polymorphism; Linear model; Genetics; Generalized linear mixed model; Mathematics; Statistics; Bayesian probability; Biology; Computer science; Gene; Genotype","retraction":null,"screen_n_in":null,"score":{"opus":0.1296291456057422,"gpt":0.3840393948224862,"spread":0.2544102492167439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001813052,0.0001205133,0.0003183263,0.00009730494,0.0000275454,0.00000393007,0.0001183971,0.0002109568,0.000005754523],"category_scores_gemma":[0.003528343,0.00009075055,0.00006641979,0.0001270621,0.00004714331,0.00001378331,0.0001148132,0.00006242973,0.000003944101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281225,"about_ca_system_score_gemma":0.00005430941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009650443,"about_ca_topic_score_gemma":0.0005644417,"domain_scores_codex":[0.9984777,0.0001951909,0.0007901011,0.0001484653,0.0001121404,0.0002763836],"domain_scores_gemma":[0.9986697,0.0005414521,0.0004672583,0.0001513014,0.0001419231,0.00002830453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000056626,0.0001307534,0.9512891,0.0001039758,0.0002001639,0.000001186516,0.001926352,0.003456333,0.02070165,0.0002277641,0.003443675,0.01846249],"study_design_scores_gemma":[0.004723814,0.0004010379,0.9584392,0.00008204591,0.00004011292,0.000003264947,0.003065506,0.005532923,0.01935696,0.003630248,0.004218046,0.0005068179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9186248,0.0003029743,0.07904494,0.00162478,0.00007086804,0.0002205763,0.00003168518,0.000006999805,0.00007239138],"genre_scores_gemma":[0.8640344,0.001753235,0.133091,0.0008659397,0.00002736875,0.00003745516,0.00005180389,0.00001025498,0.0001285536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0545904,"threshold_uncertainty_score":0.4224011,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2885079199","doi":"10.1093/bib/bby063","title":"A brief history of bioinformatics","year":2018,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":385,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut universitaire de cardiologie et de pneumologie de Québec; Institut National de la Recherche Scientifique; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science; Bioinformatics; Computational biology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.04273783761211765,"gpt":0.3099072221447389,"spread":0.2671693845326213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001324299,0.0007916644,0.001890214,0.0007633889,0.0000547525,0.0000580191,0.00142348,0.00134107,0.00007246427],"category_scores_gemma":[0.00114939,0.0006830604,0.0007459786,0.0005102481,0.001165437,0.00003087975,0.0009159713,0.0006072855,0.0001971435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004062694,"about_ca_system_score_gemma":0.002578345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006793103,"about_ca_topic_score_gemma":0.00003575153,"domain_scores_codex":[0.9943042,0.00009697124,0.003337651,0.0003850094,0.0009087727,0.0009674304],"domain_scores_gemma":[0.9962917,0.000112521,0.00160219,0.001310871,0.0003477061,0.000335042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002810376,0.0001446456,0.000005955879,0.04037278,0.000203244,0.000004072388,0.0007974557,0.000001070261,0.000005621525,0.00008456471,0.1518204,0.8065321],"study_design_scores_gemma":[0.0005628174,0.0004740378,0.000003115245,0.005075582,0.000144129,0.00007783297,0.0001027951,0.0008760297,0.0000408494,0.00002692086,0.9918962,0.0007196642],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001454839,0.9764478,0.001559816,0.00002738981,0.0007880543,0.001123808,0.0002115123,0.00003276658,0.01979437],"genre_scores_gemma":[0.000002667307,0.9741433,0.02138779,0.0004822913,0.0002987466,0.00005724178,0.0009742463,0.00008233728,0.002571429],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8400759,"threshold_uncertainty_score":0.9999554,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996902131","doi":"10.1093/bib/3.4.331","title":"BioMOBY: An open source biological web services proposal","year":2002,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":360,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Biotechnology Research Institute; National Research Council Canada; Plant Biotechnology Institute","funders":"","keywords":"Computer science; Web service; SOAP; World Wide Web; XML; Object (grammar); Database; Data integration; Database transaction; Simple (philosophy); Service (business); Information retrieval","retraction":null,"screen_n_in":null,"score":{"opus":0.1657501190205644,"gpt":0.3697145778204866,"spread":0.2039644587999221,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004994296,0.0001797936,0.0002948414,0.000387711,0.0002679325,0.002831628,0.003849384,0.00009441651,0.0006449915],"category_scores_gemma":[0.0006845338,0.0001231846,0.00004590416,0.001441584,0.0001628494,0.001643725,0.002366057,0.0001452728,0.001622173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003333595,"about_ca_system_score_gemma":0.00003070548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002509875,"about_ca_topic_score_gemma":0.0001412277,"domain_scores_codex":[0.9968754,0.0001158795,0.001072552,0.0005131455,0.0009880079,0.0004350002],"domain_scores_gemma":[0.9978436,0.0002851175,0.0003556278,0.001242174,0.0001176819,0.0001557492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003247685,0.0006199271,0.01259527,0.00005334141,0.00001734601,0.00002019047,0.01073155,0.0005599509,0.0001231123,0.00689219,0.190534,0.7778206],"study_design_scores_gemma":[0.0004565447,0.0001168855,0.002108047,0.00003550235,0.000002819028,0.0000158112,0.002238808,0.6592453,0.00002475116,0.003325165,0.332201,0.0002294071],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8901042,0.0001227611,0.01447944,0.008711965,0.001342822,0.001467188,0.00009696835,0.0004890964,0.08318555],"genre_scores_gemma":[0.8835553,0.00003786818,0.1029623,0.008378197,0.0001188016,0.00002296975,0.00007429112,0.00002181875,0.004828497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7775912,"threshold_uncertainty_score":0.9991552,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2740005242","doi":"10.1093/bib/bbx081","title":"Visualizing and comparing circular genomes using the CGView family of tools","year":2017,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":318,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Genome; Graphical user interface; Computer science; Software; Visualization; Computational biology; Biology; Genetics; Data mining; Programming language; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.1834318136619482,"gpt":0.3684504823494446,"spread":0.1850186686874964,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004560682,0.0003157124,0.001062923,0.00007610866,0.0001793183,0.0001121559,0.0004556823,0.0002163964,4.482079e-7],"category_scores_gemma":[0.0001038253,0.0002339646,0.00021994,0.00007033207,0.000224015,0.00000333806,0.0005865635,0.0001519056,9.056919e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002036759,"about_ca_system_score_gemma":0.0001540819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006787662,"about_ca_topic_score_gemma":0.000006331263,"domain_scores_codex":[0.998548,0.00004533015,0.0008264874,0.0002006538,0.0001213506,0.0002581932],"domain_scores_gemma":[0.998499,0.00003974585,0.0008020785,0.00056929,0.00005272213,0.00003715961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003521385,0.00001910592,0.0002248013,0.01664865,0.0003633472,0.000001945271,0.0006801015,0.00002839973,0.0002052539,0.00015118,0.0001220174,0.9815516],"study_design_scores_gemma":[0.0001588211,0.00003158896,0.00006375153,0.003022823,0.0002362436,0.00004934593,0.0001566469,0.0004978198,0.00002148045,0.00002649131,0.9954262,0.0003087543],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003763994,0.9948725,0.0001673999,0.000007445441,0.00009597339,0.0004531256,0.0000302404,0.000001786873,0.0006075489],"genre_scores_gemma":[0.000934022,0.9966542,0.002147806,0.00008206536,0.00008997609,0.00001543371,0.00003484834,0.00002856453,0.00001311259],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9953042,"threshold_uncertainty_score":0.9540799,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3160021293","doi":"10.1093/bib/bbab159","title":"Utilizing graph machine learning within drug discovery and development","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Drug repositioning; Repurposing; Computer science; Drug discovery; Pipeline (software); Context (archaeology); Data science; Identification (biology); Drug development; Machine learning; Artificial intelligence; Key (lock); Drug; Bioinformatics; Engineering; Medicine; Pharmacology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.02108290369665256,"gpt":0.2570484905692298,"spread":0.2359655868725773,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008423902,0.0001679214,0.0002057489,0.0001826077,0.0001739524,0.0005307464,0.0003127744,0.0000414001,0.000002778822],"category_scores_gemma":[0.0002983941,0.0001700177,0.00003350645,0.0005647808,0.00004993044,0.001608939,0.0007068947,0.0002821097,0.000008225008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006194988,"about_ca_system_score_gemma":0.0003083996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004505246,"about_ca_topic_score_gemma":0.0000574444,"domain_scores_codex":[0.9984778,0.00009409195,0.0005719877,0.0002399181,0.0003515011,0.0002647113],"domain_scores_gemma":[0.9991344,0.0003319702,0.0001784913,0.0002200459,0.00006442949,0.00007067053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002835105,0.0002713816,0.02585485,0.0009551399,0.0001248829,0.0002273784,0.1066077,0.1355795,0.0002525726,0.32372,0.0006131576,0.4057651],"study_design_scores_gemma":[0.0005996447,0.00001391833,0.01008353,0.0002060513,0.000003955349,0.0001651604,0.0008857413,0.9691797,0.003723295,0.007017006,0.00768824,0.0004337572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1586056,0.000507661,0.8380101,0.0008475318,0.0002189478,0.0001089484,0.000002104565,0.0001169549,0.001582203],"genre_scores_gemma":[0.1264164,0.00006188513,0.8720194,0.001269923,0.00001215924,0.00000709193,0.00002011195,0.00001090223,0.0001821606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8336002,"threshold_uncertainty_score":0.6933119,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1974896839","doi":"10.1093/bib/bbv011","title":"The role of ontologies in biological and biomedical research: a functional perspective","year":2015,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":279,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Association of Occupational Therapists; Saint Paul University","funders":"","keywords":"Computer science; Biomedicine; Open Biomedical Ontologies; Metadata; Ontology; Controlled vocabulary; Data science; Domain (mathematical analysis); Perspective (graphical); Meaning (existential); Identifier; Vocabulary; Biological data; IDEF5; Information retrieval; Knowledge management; Artificial intelligence; Domain knowledge; World Wide Web; Ontology-based data integration; Ontology alignment; Epistemology; Bioinformatics","retraction":null,"screen_n_in":null,"score":{"opus":0.09409572770032755,"gpt":0.3389545872947298,"spread":0.2448588595944022,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001369398,0.00008483508,0.0001397975,0.0001009679,0.00005699931,0.00001973815,0.0001784894,0.0002233918,0.00000183878],"category_scores_gemma":[0.003479413,0.00005358492,0.00002527725,0.0002251765,0.001357914,0.00000481951,0.000252067,0.0002149471,0.000002777053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003570738,"about_ca_system_score_gemma":0.0001489349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000189008,"about_ca_topic_score_gemma":0.0001462232,"domain_scores_codex":[0.9989482,0.00008867869,0.00031638,0.0001355267,0.0002348877,0.0002762895],"domain_scores_gemma":[0.9993863,0.0001980674,0.00006581414,0.0001473421,0.0001304729,0.00007200205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00290298,0.001106748,0.1863077,0.0001289407,0.0002029347,0.00003672999,0.02691645,0.0000486175,0.0209232,0.05906045,0.03508746,0.6672778],"study_design_scores_gemma":[0.004484017,0.003594741,0.125603,0.0001859914,0.000009609428,0.0001925569,0.1802307,0.005040173,0.008035076,0.0787024,0.5932841,0.0006376473],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859607,0.006120837,0.0003982462,0.002796585,0.00009545086,0.0001917242,0.00001005982,0.00001242067,0.004413952],"genre_scores_gemma":[0.9946087,0.0005977018,0.004557947,0.0001184644,0.00004330349,0.00001795617,0.00001361604,0.000004176182,0.00003814824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6666401,"threshold_uncertainty_score":0.5003291,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2568779902","doi":"10.1093/bib/bbw112","title":"A review of connectivity map and computational approaches in pharmacogenomics","year":2017,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":273,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Institute of Cancer Research; Ontario Institute for Cancer Research; Princess Margaret Cancer Centre; University Health Network","funders":"National Center for Research Resources; National Institute of General Medical Sciences; Engineering and Physical Sciences Research Council; National Institutes of Health; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Ministero dello Sviluppo Economico; Kansainvälisen Liikkuvuuden ja Yhteistyön Keskus; Biotechnology and Biological Sciences Research Council; Ulster University; Public Health Agency; Natural Sciences and Engineering Research Council of Canada; European Regional Development Fund; European Commission","keywords":"Pharmacogenomics; Computational biology; Systems biology; Computer science; Computational model; Data science; Biology; Bioinformatics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.08525815075261269,"gpt":0.3318957408863297,"spread":0.246637590133717,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009067538,0.0003468137,0.001261959,0.0001468504,0.00004806454,0.00004016549,0.0003792618,0.0003555381,0.000003621302],"category_scores_gemma":[0.000112181,0.0003230404,0.0002020623,0.00009369408,0.0001789099,0.0000153401,0.0003598844,0.0003069867,0.000004360438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003969797,"about_ca_system_score_gemma":0.0003091449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000206854,"about_ca_topic_score_gemma":0.00001804592,"domain_scores_codex":[0.9980466,0.00005359847,0.001304469,0.0002139116,0.0001158812,0.0002654746],"domain_scores_gemma":[0.9983081,0.00006013037,0.001122499,0.0004046911,0.0000412699,0.00006333138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006470349,0.00003776485,0.00001093344,0.1524861,0.00006891051,0.00000124093,0.00007599365,0.0000370008,1.833263e-7,0.0002079205,0.002076022,0.8449914],"study_design_scores_gemma":[0.0004163616,0.00004009383,0.000007356869,0.04320419,0.00009833624,0.00006401345,0.0000133126,0.003362473,0.000001370785,0.0001650637,0.9522589,0.0003685158],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001537126,0.9972328,0.00072792,0.00005094134,0.00007745915,0.0008185943,0.0001220581,0.000003677937,0.0009511554],"genre_scores_gemma":[0.00002303613,0.9923442,0.006401686,0.0004973444,0.00005010437,0.00004320622,0.0005910065,0.00002509903,0.0000242963],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9501829,"threshold_uncertainty_score":0.9999222,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2758894305","doi":"10.1093/bib/bbx121","title":"PHAST, PHASTER and PHASTEST: Tools for finding prophage in bacterial genomes","year":2017,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":235,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Genome Alberta","keywords":"Prophage; Metagenomics; Successor cardinal; Computer science; Genome; World Wide Web; Popularity; Bacterial genome size; Web server; Computational biology; Biology; Bacteriophage; Genetics; The Internet; Gene; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.1267825289350761,"gpt":0.3647686394301612,"spread":0.2379861104950851,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003482138,0.0004110589,0.00104284,0.0001678286,0.000170856,0.0006539826,0.0004256229,0.0002765172,0.0003348051],"category_scores_gemma":[0.000139772,0.0003606171,0.0001836432,0.0001224031,0.000165376,0.001527531,0.000376435,0.0003269799,0.0001158892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002750407,"about_ca_system_score_gemma":0.00005381236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001106153,"about_ca_topic_score_gemma":0.0001433916,"domain_scores_codex":[0.9980663,0.00002590339,0.001013038,0.0003054205,0.0001085721,0.0004807413],"domain_scores_gemma":[0.9986886,0.0001852067,0.0006901065,0.0003494196,0.000006856629,0.00007977302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009433964,0.00004662341,0.00000542624,0.002734597,0.00001391806,0.000005605138,0.0004877431,8.085649e-7,0.00004692063,0.000007032243,0.0005052867,0.9961366],"study_design_scores_gemma":[0.0006937665,0.00005869885,0.00007852988,0.003560924,0.00006856076,0.0001063129,0.00002667582,0.0001429553,0.000008138286,0.0000124157,0.9948534,0.0003896519],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00272587,0.9740115,0.0007793257,0.0002051856,0.001710043,0.007984534,0.001058347,0.00008917,0.01143603],"genre_scores_gemma":[0.00007131785,0.9886895,0.009404561,0.0001612227,0.0001434907,0.0002604213,0.0002178102,0.00005768131,0.0009939491],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.995747,"threshold_uncertainty_score":0.9998846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983624870","doi":"10.1093/bib/bbm030","title":"Current Progress in computational metabolomics","year":2007,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":227,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Institute for Nanotechnology","funders":"","keywords":"Metabolomics; Cheminformatics; Computer science; Field (mathematics); Data science; Computational model; Computational biology; Bioinformatics; Artificial intelligence; Biology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0384868866720468,"gpt":0.3516625370312874,"spread":0.3131756503592406,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006793739,0.0004540824,0.001211609,0.00055983,0.00004540935,0.00005296295,0.0003754106,0.0003654908,0.000005246112],"category_scores_gemma":[0.0001106683,0.0004078226,0.0002772536,0.0005879994,0.0001378252,0.000008355796,0.0003344474,0.0004412107,0.00001816899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000766849,"about_ca_system_score_gemma":0.0002626594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006169794,"about_ca_topic_score_gemma":0.00002663168,"domain_scores_codex":[0.9975834,0.00004655969,0.001305527,0.0003282454,0.0002407677,0.000495459],"domain_scores_gemma":[0.9989853,0.00004922887,0.000517039,0.0003117298,0.00006731122,0.00006937143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000796019,0.00009549219,0.0001498191,0.004404494,0.00007439421,0.000003734696,0.0000528515,0.00001492329,1.361072e-7,0.001364053,0.00105305,0.9927791],"study_design_scores_gemma":[0.0003701283,0.00003824049,0.0001169333,0.001314744,0.00007048176,0.00002660928,0.00001667717,0.0001839236,0.000002786356,0.0001304364,0.9972976,0.0004313941],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008707245,0.9974833,0.0009939289,0.00002731475,0.0002935212,0.0005988058,0.00007311044,0.00001125603,0.0004317424],"genre_scores_gemma":[0.00001630956,0.9826627,0.0162106,0.0001296772,0.0001267702,0.00006899517,0.0007218165,0.00003840731,0.00002476137],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9962446,"threshold_uncertainty_score":0.9998373,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2128965269","doi":"10.1093/bib/bbr069","title":"Hive plots--rational approach to visualizing networks","year":2011,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":222,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"BC Cancer Agency; BC Cancer Foundation; Canada's Michael Smith Genome Sciences Centre","keywords":"Plug-in; Computer science; Node (physics); Simple (philosophy); Data mining; Theoretical computer science; Artificial intelligence; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.031427913772389,"gpt":0.2552130692557583,"spread":0.2237851554833693,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002479402,0.0001732564,0.0002345717,0.0001711428,0.00009160844,0.00006878994,0.0002690516,0.00004686165,0.0001464075],"category_scores_gemma":[0.000008147528,0.0001750218,0.00009020282,0.0004714795,0.00003713258,0.0002649715,0.0001586099,0.0001741464,0.00003521501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003823819,"about_ca_system_score_gemma":0.00002974161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003814558,"about_ca_topic_score_gemma":0.000005867123,"domain_scores_codex":[0.9988022,0.00001951576,0.0005235796,0.0001501874,0.0001813881,0.0003231631],"domain_scores_gemma":[0.9994034,0.00003743613,0.0001607945,0.0002481867,0.00006122005,0.00008899326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005396346,0.0007710783,0.04930566,0.0000595718,0.0002304154,0.00000166618,0.01848078,0.01100567,0.00002307077,0.786787,0.06329194,0.06998919],"study_design_scores_gemma":[0.0004474999,0.00006473477,0.01003774,0.00009882099,0.00004227458,0.000003126512,0.0011306,0.9602761,0.0003171246,0.01362464,0.0132762,0.0006810881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00697715,0.00001141924,0.8144458,0.00003038699,0.00003267976,0.0002624278,0.000005525353,0.00009914852,0.1781355],"genre_scores_gemma":[0.7348212,0.000002174503,0.2639742,0.0007829204,0.0001486819,0.00006649963,0.00008155197,0.00002054727,0.0001021734],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9492705,"threshold_uncertainty_score":0.7137183,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2997680655","doi":"10.1093/bib/bbz152","title":"DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features","year":2019,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":212,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Society of Canada; University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Random forest; DrugBank; Artificial intelligence; Machine learning; Artificial neural network; Similarity (geometry); Data mining; Drug","retraction":null,"screen_n_in":null,"score":{"opus":0.01879141873420673,"gpt":0.2637287432124038,"spread":0.2449373244781971,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000580055,0.0001770059,0.0002096634,0.0002787063,0.00008687894,0.0001329939,0.0007583357,0.00004301848,0.000007275165],"category_scores_gemma":[0.0001998989,0.0001374913,0.0001157916,0.0004516729,0.00006001165,0.0008817298,0.000217715,0.0003210734,0.00001897805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001238938,"about_ca_system_score_gemma":0.0002287279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001120603,"about_ca_topic_score_gemma":0.00002644908,"domain_scores_codex":[0.9984032,0.00007572219,0.0005445005,0.0001916063,0.0005392066,0.0002457517],"domain_scores_gemma":[0.9984634,0.0005204676,0.000275564,0.0005776428,0.0001130815,0.00004986492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001941299,0.00007585313,0.000310889,0.00006899312,0.0000104986,8.763429e-7,0.002185406,0.9742783,0.00001341195,0.01021853,0.001846499,0.01097129],"study_design_scores_gemma":[0.0004189596,0.0000460905,0.006688945,0.00009955214,0.000005670517,0.00002024449,0.00008633864,0.9830996,0.001291046,0.0073233,0.0007954476,0.0001247511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.068954,0.00001857943,0.9222726,0.002343847,0.0004071495,0.0004190306,0.00004676043,0.00007876488,0.005459241],"genre_scores_gemma":[0.6887446,0.000004741855,0.3089109,0.002129516,0.00002468905,0.00002382913,0.00002784034,0.00001123876,0.0001226641],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6197906,"threshold_uncertainty_score":0.5606734,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005970824","doi":"10.1093/bib/bbz176","title":"Biomedical data and computational models for drug repositioning: a comprehensive review","year":2020,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":200,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Drug repositioning; Computer science; Machine learning; Benchmark (surveying); Big data; Artificial intelligence; Drug discovery; Data mining; Data science; Drug; Bioinformatics; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.1235499443295014,"gpt":0.3901579760297585,"spread":0.2666080317002572,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009636024,0.0004537403,0.001719553,0.0002888035,0.0001339417,0.0003391867,0.002037627,0.0001512468,0.00000133025],"category_scores_gemma":[0.0005041487,0.00042232,0.0002035472,0.0009328008,0.0001430649,0.00133505,0.002268326,0.0004133082,0.00001163651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001175107,"about_ca_system_score_gemma":0.0009666355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001025797,"about_ca_topic_score_gemma":4.388563e-7,"domain_scores_codex":[0.9963322,0.0002061968,0.001834727,0.0006839769,0.0006131456,0.000329759],"domain_scores_gemma":[0.9953785,0.002465084,0.0009179479,0.0008693932,0.0001802143,0.0001888811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001345788,0.00002803581,3.474406e-8,0.06575134,0.00006071014,0.0000117061,0.0002745298,0.0007899138,1.835059e-9,0.01542496,0.01568939,0.9019681],"study_design_scores_gemma":[0.000135273,0.00001387681,1.620493e-7,0.0169338,0.00007702823,0.0002008797,0.000005485963,0.4879842,6.081253e-9,0.006379585,0.4880456,0.000224139],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.373379e-8,0.5203576,0.4770404,0.001283761,0.00008872359,0.0008812696,0.0002394053,0.00005517176,0.00005366711],"genre_scores_gemma":[5.649051e-8,0.5508557,0.4456213,0.002135742,0.00003517241,0.000061224,0.001271284,0.00001611968,0.000003345072],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9017439,"threshold_uncertainty_score":0.9998229,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1973247012","doi":"10.1093/bib/bbt039","title":"Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks","year":2013,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":185,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Program for New Century Excellent Talents in University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Ministry of Education of the People's Republic of China; National Science Foundation","keywords":"Computer science; Identification (biology); Computational biology; Biological network; Artificial intelligence; Theoretical computer science; Data mining; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.02495588995162475,"gpt":0.2703320226732798,"spread":0.245376132721655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004962629,0.0008764719,0.001450616,0.0002583633,0.000211684,0.0004664983,0.0005460039,0.0009393637,0.00004066135],"category_scores_gemma":[0.00007161028,0.0008086269,0.0003125786,0.0003833215,0.0001715713,0.00004598577,0.0008327784,0.0007654301,0.00006625697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001261499,"about_ca_system_score_gemma":0.000155965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000144855,"about_ca_topic_score_gemma":0.0001161191,"domain_scores_codex":[0.9962178,0.00009109123,0.001937067,0.0005602844,0.0003162145,0.0008775831],"domain_scores_gemma":[0.9978047,0.00009143481,0.0008898097,0.0007955787,0.0001053816,0.000313085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002554571,0.00004724661,0.0000059186,0.00435167,0.0003222573,0.000004170348,0.0001885088,0.005983019,0.00001350468,0.0002254881,0.0137955,0.9750372],"study_design_scores_gemma":[0.0006706362,0.0001627057,0.00006771352,0.007153844,0.0002412143,0.00007706972,0.0001308545,0.2545852,0.000003284124,0.0004761688,0.7349477,0.001483646],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003041174,0.7375466,0.2589039,0.00005853472,0.0003947935,0.00233819,0.000158602,0.00004072937,0.0002545487],"genre_scores_gemma":[0.0003368424,0.9565733,0.03676343,0.001143754,0.0003982563,0.0004235542,0.003800845,0.000135392,0.0004245949],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9735535,"threshold_uncertainty_score":0.9994364,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3205082786","doi":"10.1093/bib/bbab421","title":"MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":183,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Shanghai Jiao Tong University; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Feature learning; Drug; Feature (linguistics); Artificial intelligence; Encoder; Fusion; Transformer; Mechanism (biology); Fusion mechanism; Pattern recognition (psychology); Machine learning; Medicine; Pharmacology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01347361446730321,"gpt":0.2656193314134141,"spread":0.2521457169461109,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001438231,0.0004419107,0.0003989957,0.000462961,0.0004427231,0.0003882525,0.0005278283,0.0001611807,0.00001461511],"category_scores_gemma":[0.000369433,0.000447255,0.0001816921,0.0009021287,0.00003808516,0.00168425,0.0003247819,0.0007107466,0.00002273509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002752251,"about_ca_system_score_gemma":0.0001995642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001533498,"about_ca_topic_score_gemma":0.0001098955,"domain_scores_codex":[0.9967086,0.0003569376,0.0009101112,0.0005996554,0.0009066144,0.000518086],"domain_scores_gemma":[0.9977811,0.0007576636,0.0004852287,0.0005182343,0.0002698574,0.0001879186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003250687,0.003680197,0.009058684,0.002632203,0.0002496548,0.0001004808,0.1214093,0.3391659,0.005575537,0.003571684,0.00308765,0.5111436],"study_design_scores_gemma":[0.002241638,0.00003553818,0.006691039,0.0006442728,0.00002894609,0.00007907052,0.001099698,0.9828401,0.002772304,0.0002465862,0.002865853,0.000454954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2010643,0.00004688016,0.7946356,0.002717481,0.0005910759,0.0004846264,0.00001466881,0.0002835882,0.0001618336],"genre_scores_gemma":[0.2782252,0.0000893032,0.717593,0.003126359,0.00007725265,0.0000380577,0.0001287556,0.00005048704,0.0006715989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6436742,"threshold_uncertainty_score":0.9997979,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2742478344","doi":"10.1093/bib/bbx100","title":"A global perspective on evolving bioinformatics and data science training needs","year":2017,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":177,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Institute for Cancer Research","funders":"Natural Environment Research Council; Medical Research Council; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Globe; Stewardship (theology); Trainer; Training (meteorology); Curriculum; Perspective (graphical); Appeal; Engineering ethics; Principal (computer security); Interpretation (philosophy); Personalization; Computer science; Data science; Political science; Medicine; Psychology; Artificial intelligence; World Wide Web; Engineering; Pedagogy","retraction":null,"screen_n_in":null,"score":{"opus":0.05193494988416302,"gpt":0.352883202144799,"spread":0.3009482522606359,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001705844,0.0002983345,0.0002975338,0.000291549,0.0009217456,0.0008762408,0.002279375,0.0002267605,0.000007287323],"category_scores_gemma":[0.004821381,0.0002669824,0.00005136316,0.0003296876,0.001876324,0.0002198258,0.002378739,0.0002481446,0.00002475939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164361,"about_ca_system_score_gemma":0.0006697819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002418854,"about_ca_topic_score_gemma":0.0001500606,"domain_scores_codex":[0.9973242,0.00001717051,0.0006777058,0.0003489055,0.0008071571,0.0008248994],"domain_scores_gemma":[0.9970905,0.00004001461,0.0003801566,0.001845858,0.0002937114,0.0003497452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007395872,0.0007247213,0.03499687,0.001769255,0.0004082152,0.0000579983,0.04797133,0.0002166692,0.004934416,0.03645831,0.03570608,0.8360165],"study_design_scores_gemma":[0.009205602,0.003036344,0.1401437,0.001327606,0.0001090532,0.0005921639,0.07954668,0.6799302,0.004917242,0.004299591,0.07355378,0.003338098],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6770812,0.0008797567,0.01672185,0.008104244,0.001441622,0.001990307,0.000880603,0.0001306062,0.2927698],"genre_scores_gemma":[0.9556684,0.0008339148,0.04130439,0.001740177,0.0001814463,0.000009303093,0.0001104246,0.00001977086,0.0001321366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8326784,"threshold_uncertainty_score":0.9999782,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2130208436","doi":"10.1093/bib/bbr009","title":"A large-scale benchmark study of existing algorithms for taxonomy-independent microbial community analysis","year":2011,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":168,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Society of Microbiologists","funders":"National Center for Advancing Translational Sciences; National Institutes of Health; University of Michigan; National Institute of Diabetes and Digestive and Kidney Diseases; W. M. Keck Foundation; Alfred P. Sloan Foundation","keywords":"Benchmark (surveying); Cluster analysis; Computer science; Data mining; Hierarchical clustering; Scale (ratio); Machine learning; Algorithm; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.07934565095866285,"gpt":0.3035607196679215,"spread":0.2242150687092587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008357274,0.0001657586,0.0003427741,0.0001788212,0.0001776049,0.00001716399,0.0003120181,0.0001525781,0.00001624478],"category_scores_gemma":[0.00006420352,0.0001697006,0.0001415031,0.0002876684,0.0000500505,0.00001017533,0.0002215321,0.0001864302,0.000001414374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002735867,"about_ca_system_score_gemma":0.00007403931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001339705,"about_ca_topic_score_gemma":0.003900328,"domain_scores_codex":[0.9986498,0.00007049731,0.0007003318,0.000149555,0.00009105916,0.0003387593],"domain_scores_gemma":[0.9989934,0.00002532233,0.0003470061,0.0004444165,0.0001257921,0.00006403661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002072911,0.02224549,0.5105826,0.003742178,0.004853956,0.00001187397,0.2940113,0.0006863253,0.1291748,0.0004563693,0.01247309,0.01968912],"study_design_scores_gemma":[0.02852203,0.01114197,0.5748315,0.0003515227,0.002502556,0.00008612406,0.1942339,0.02624134,0.103681,0.0002809156,0.0546964,0.003430779],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770614,0.00003149287,0.02047599,0.000009049747,0.00005878733,0.000784042,0.0001218811,0.000008812013,0.001448529],"genre_scores_gemma":[0.9466692,0.00001310414,0.05259411,0.0003012634,0.00002883019,0.0000565527,0.0002562841,0.00001333512,0.00006730785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09977739,"threshold_uncertainty_score":0.692019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312197939","doi":"10.1093/bib/bbac553","title":"Comprehensive investigation of pathway enrichment methods for functional interpretation of LC–MS global metabolomics data","year":2022,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":161,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Cancer Institute; National Institutes of Health","keywords":"Metabolomics; Annotation; Computer science; Computational biology; Benchmark (surveying); Metabolite; Ranking (information retrieval); Data mining; Biology; Bioinformatics; Artificial intelligence; Biochemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.03885097175928597,"gpt":0.318289618969351,"spread":0.2794386472100651,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006502747,0.00012077,0.000276866,0.00008183528,0.00006887668,0.000007931004,0.0002817714,0.00005222258,0.000007379389],"category_scores_gemma":[0.0002341171,0.0001248191,0.00007085523,0.0002374848,0.00009126488,0.00001534038,0.0006195898,0.00006651437,1.618334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003596291,"about_ca_system_score_gemma":0.00011711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004163876,"about_ca_topic_score_gemma":0.000007402857,"domain_scores_codex":[0.9988356,0.00007404453,0.0006130508,0.0001788133,0.0001552338,0.0001432963],"domain_scores_gemma":[0.9989353,0.00006947105,0.0004654279,0.0003544199,0.0001487591,0.00002664172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001880926,0.0004739637,0.01436475,0.001601006,0.001371719,5.79791e-7,0.003108599,0.01253179,0.7639474,0.04300563,0.01584751,0.1418661],"study_design_scores_gemma":[0.006512515,0.002199455,0.03959443,0.00006766865,0.0003439086,0.00004982809,0.005166101,0.2744605,0.3290152,0.01322382,0.3282418,0.001124805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4315439,0.002610155,0.5623698,0.0002917484,0.0005195111,0.0007187615,0.001474476,0.000009738014,0.0004619542],"genre_scores_gemma":[0.5320067,0.0003441235,0.4641234,0.0008083704,0.00004297951,0.00008433703,0.002550459,0.00001333608,0.00002627218],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4349323,"threshold_uncertainty_score":0.5089974,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123375001","doi":"10.1093/bib/bbp001","title":"A survey of available tools and web servers for analysis of protein-protein interactions and interfaces","year":2008,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":158,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institute of Aging","funders":"National Cancer Institute; National Institutes of Health; Türkiye Bilimler Akademisi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; U.S. Department of Health and Human Services","keywords":"Server; Computer science; Function (biology); Web server; Data science; World Wide Web; The Internet; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.04070044225562217,"gpt":0.2574834218681279,"spread":0.2167829796125058,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003805538,0.0001196463,0.0003036125,0.0001621575,0.00004831087,0.00001936745,0.0001036769,0.00009959279,0.000007999246],"category_scores_gemma":[0.0001771131,0.0001147031,0.00005685984,0.0002686197,0.0001566968,0.00002644657,0.0001181052,0.00006573528,5.083531e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008298561,"about_ca_system_score_gemma":0.00006657506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002978963,"about_ca_topic_score_gemma":0.0005670178,"domain_scores_codex":[0.9990668,0.00001837593,0.000579518,0.0001080894,0.00007391542,0.000153283],"domain_scores_gemma":[0.9992625,0.00004753346,0.0003261998,0.0001919295,0.0001304427,0.00004141483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003484978,0.001095609,0.2286787,0.00849561,0.009354419,0.000004492765,0.01651784,0.004763228,0.5896863,0.003210658,0.01978101,0.1149271],"study_design_scores_gemma":[0.006981718,0.002167812,0.1617106,0.0008400609,0.0007204335,0.00006287045,0.001545904,0.5730445,0.2207625,0.0003157076,0.03011478,0.001733136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947054,0.0003473044,0.003749639,0.00003481972,0.00001817203,0.0004085418,0.0003070327,0.000003537179,0.0004255158],"genre_scores_gemma":[0.9884228,0.0002291178,0.01077528,0.00007785798,0.000006456098,0.0000233182,0.0001951583,0.000007739803,0.0002623069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5682812,"threshold_uncertainty_score":0.4677458,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2776619248","doi":"10.1093/bib/bbx167","title":"Multi-omics integration—a comparison of unsupervised clustering methodologies","year":2017,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":150,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"Provincia Autonoma di Trento","keywords":"Cluster analysis; Computer science; Preprocessor; Data mining; Feature selection; Artificial intelligence; Data integration; Machine learning; Similarity (geometry); Pattern recognition (psychology); Noise (video)","retraction":null,"screen_n_in":null,"score":{"opus":0.07452396971029684,"gpt":0.345032591346865,"spread":0.2705086216365682,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005379965,0.0001792076,0.0003216522,0.0000708498,0.0001743552,0.0001092286,0.0005839861,0.0002342095,0.000004819505],"category_scores_gemma":[0.0004438896,0.0001671299,0.00009225009,0.00004358999,0.0002018221,0.00002891445,0.0004211018,0.0001653632,0.000004757147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001769286,"about_ca_system_score_gemma":0.00005155935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008724947,"about_ca_topic_score_gemma":0.000217163,"domain_scores_codex":[0.9987087,0.0000240063,0.0007714232,0.0001270521,0.0001138283,0.0002549363],"domain_scores_gemma":[0.9985652,0.00003320795,0.0005857452,0.0006808143,0.00008605426,0.0000489521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000516163,0.000387979,0.03262954,0.001114252,0.000296706,0.000003381264,0.01410528,0.01605396,0.1353554,0.002157357,0.00638843,0.7909915],"study_design_scores_gemma":[0.002734913,0.0003037338,0.02416146,0.000218774,0.00003452136,0.00001728181,0.002888936,0.8724543,0.0891896,0.000523643,0.00678971,0.0006830825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2087264,0.0003949154,0.7852203,0.0004217982,0.0004728748,0.0005000785,0.00006196977,0.00002770138,0.004173958],"genre_scores_gemma":[0.6007721,0.0001629917,0.3985899,0.0002776233,0.00004197684,0.000007689992,0.00005827916,0.00001324282,0.00007616862],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8564004,"threshold_uncertainty_score":0.681536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3028083463","doi":"10.1093/bib/bbaa232","title":"Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research","year":2020,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Wellcome Trust; Biotechnology and Biological Sciences Research Council; Intramural Research Program; Agence Nationale de la Recherche; National Institute of Allergy and Infectious Diseases; Medical Research Council; National Institutes of Health; Fondation Innovations en Infectiologie; Max-Planck-Gesellschaft; U.S. National Library of Medicine; Consejo Nacional de Investigaciones Científicas y Técnicas; National Institute of General Medical Sciences; Bundesministerium für Bildung und Forschung; National Human Genome Research Institute; Horizon 2020 Framework Programme; Agencia Nacional de Promoción Científica y Tecnológica; Staatssekretariat für Bildung, Forschung und Innovation; Deutsche Forschungsgemeinschaft; European Commission; European Bioinformatics Institute; Joachim Herz Stiftung; European Molecular Biology Laboratory; Velux Fonden; Carl-Zeiss-Stiftung","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Coronavirus; Betacoronavirus; Pandemic; Virology; Coronavirus Infections; Sars virus; Computer science; Medicine; Infectious disease (medical specialty); Disease; Outbreak","retraction":null,"screen_n_in":null,"score":{"opus":0.4185659936969426,"gpt":0.5144904201730778,"spread":0.09592442647613514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001512667,0.0006795357,0.002340708,0.001284272,0.0002619739,0.001018194,0.0005848019,0.0004636917,0.00003253021],"category_scores_gemma":[0.004428005,0.0006425817,0.0002144025,0.0022193,0.000262076,0.0004838261,0.0009040785,0.001314931,0.0005350658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00143568,"about_ca_system_score_gemma":0.005468281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007139244,"about_ca_topic_score_gemma":0.00009808665,"domain_scores_codex":[0.9952561,0.0002107482,0.001801151,0.000653395,0.001247459,0.0008311696],"domain_scores_gemma":[0.9948749,0.00298447,0.000364102,0.0005977114,0.0003574809,0.0008213448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001383107,0.0001822171,0.00003208363,0.07254691,0.0002021922,0.0003413213,0.005975178,0.0005428006,0.000003565517,0.001711063,0.2616896,0.6566347],"study_design_scores_gemma":[0.0008538497,0.0003935529,0.00007661273,0.01170846,0.0001698891,0.0002111665,0.0002507036,0.001149408,0.000007505546,0.0002100983,0.9843857,0.000583111],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004932478,0.8835856,0.006433654,0.09467048,0.0004000589,0.01188618,0.0009542525,0.0005548975,0.001021676],"genre_scores_gemma":[0.00003965683,0.7919257,0.02480252,0.1820294,0.0001619595,0.0003799204,0.0004897856,0.0001302359,0.00004084406],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7226961,"threshold_uncertainty_score":0.9996026,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1968839521","doi":"10.1093/bib/bbv019","title":"Optimization of miRNA-seq data preprocessing","year":2015,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":141,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; Artificial Intelligence in Medicine (Canada); University of Toronto","funders":"Canadian Cancer Society Research Institute","keywords":"Computer science; Preprocessor; Count data; Data mining; Normalization (sociology); Quantile; Computational biology; Statistics; Poisson distribution; Artificial intelligence; Biology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04211552848997092,"gpt":0.2862784146327305,"spread":0.2441628861427595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003058508,0.00008122039,0.0000965032,0.00005279965,0.00001643209,0.00002200054,0.0002781076,0.00009103063,0.000004326858],"category_scores_gemma":[0.0003804726,0.0000844509,0.00001702339,0.0001276602,0.00005302622,0.00002984512,0.0002487954,0.0000353101,0.000002730473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001700243,"about_ca_system_score_gemma":0.00015713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002474324,"about_ca_topic_score_gemma":0.000006437858,"domain_scores_codex":[0.9992452,0.00001378506,0.0003539929,0.0001301467,0.0001404643,0.0001164476],"domain_scores_gemma":[0.999101,0.00000525396,0.000207604,0.000519788,0.0001141407,0.00005215821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008556922,0.0009029775,0.02823619,0.002902963,0.000254622,0.000007654692,0.006049751,0.6893771,0.08926373,0.0006783214,0.09981513,0.08165584],"study_design_scores_gemma":[0.001935507,0.0001187297,0.001994052,0.000213875,0.00003588829,0.000034603,0.0004202561,0.9141006,0.06246535,0.0002306088,0.01802333,0.0004272394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.47758,0.002378936,0.5099686,0.0004588411,0.0002364707,0.0006460704,0.0001517985,0.00005318759,0.008526077],"genre_scores_gemma":[0.7170693,0.00008856948,0.2803596,0.0004367861,0.00008844608,0.000006820234,0.001824025,0.00002718456,0.00009928233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2394893,"threshold_uncertainty_score":0.3443808,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2150196286","doi":"10.1093/bib/bbr008","title":"Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)","year":2011,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Receiver operating characteristic; Ranking (information retrieval); Metric (unit); Gold standard (test); Confidence interval; Statistics; Performance metric; Computer science; Machine learning; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1267116161059898,"gpt":0.3407471400724225,"spread":0.2140355239664327,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002366798,0.00009893769,0.0002878247,0.0006055884,0.00004882835,0.00009119177,0.0004451882,0.0001131024,0.000003581136],"category_scores_gemma":[0.0002737991,0.00009327553,0.00003872806,0.001680873,0.0001770156,0.000466071,0.0004466394,0.0002895318,0.000006240494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005691844,"about_ca_system_score_gemma":0.00005577309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004794655,"about_ca_topic_score_gemma":0.0004067531,"domain_scores_codex":[0.998601,0.00007983766,0.0005277742,0.0002201509,0.0002902686,0.0002810281],"domain_scores_gemma":[0.9989445,0.000270501,0.0001471299,0.0004182032,0.0001229273,0.00009678256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001850571,0.000363882,0.6453714,0.0006282496,0.0002475155,0.00001915782,0.1041699,0.00008867734,0.0006848801,0.01053704,0.002703226,0.2350011],"study_design_scores_gemma":[0.001585763,0.0008903352,0.874513,0.0003323257,0.00004186556,0.00003961679,0.00138396,0.09952153,0.009044178,0.009913795,0.002124244,0.0006094176],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8420131,0.00006915391,0.1544905,0.001291585,0.00008740427,0.0003433766,0.000003474871,0.00002901515,0.001672405],"genre_scores_gemma":[0.863744,0.0001477123,0.1357143,0.0003139288,0.00001066549,0.00001032792,4.164774e-7,0.000004872081,0.00005376257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2343916,"threshold_uncertainty_score":0.3803666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155983825","doi":"10.1093/bib/bbt001","title":"Using GBrowse 2.0 to visualize and share next-generation sequence data","year":2013,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Institute for Cancer Research","funders":"U.S. Public Health Service; National Institutes of Health; National Human Genome Research Institute; Ontario Institute for Cancer Research","keywords":"Panning (audio); Zoom; Computer science; Upload; Track (disk drive); Software deployment; World Wide Web; Genome browser; Genome; Genomics; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.1746980758964073,"gpt":0.3312665393608534,"spread":0.1565684634644461,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001286726,0.0001234519,0.0001136891,0.00004310697,0.00007681649,0.0001142827,0.0002205091,0.00007505149,0.000008575502],"category_scores_gemma":[0.0001169829,0.0001214737,0.0000134382,0.00008306042,0.00004154323,0.00001154061,0.0005175199,0.00004228217,0.000009023474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001164084,"about_ca_system_score_gemma":0.0000378302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003177887,"about_ca_topic_score_gemma":0.00006167484,"domain_scores_codex":[0.9992446,0.00001146381,0.0002682686,0.0001984272,0.00007974786,0.0001974965],"domain_scores_gemma":[0.9994189,0.000007458327,0.00006756787,0.0003767931,0.00006060909,0.00006866474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007010296,0.00001970287,0.002868971,0.00005867119,0.00002723379,7.683576e-7,0.0007202967,0.0003713398,0.9681722,0.0001075629,0.01031003,0.01733623],"study_design_scores_gemma":[0.001694243,0.0005352745,0.01864081,0.000180713,0.00005647885,0.0001507401,0.001474919,0.7322392,0.076171,0.0003561466,0.1668047,0.001695729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954838,0.0004657444,0.002993329,0.0003999948,0.00006398654,0.0002946047,0.00006551662,0.000003626911,0.0002293699],"genre_scores_gemma":[0.890521,0.0004378775,0.1046644,0.003937196,0.0001154709,0.00001978572,0.000239347,0.00001769389,0.0000472086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8920012,"threshold_uncertainty_score":0.4953556,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3132689447","doi":"10.1093/bib/bbac404","title":"A review of biomedical datasets relating to drug discovery: a knowledge graph perspective","year":2022,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"University of Cambridge; AstraZeneca","keywords":"Drug discovery; Computer science; Drug repositioning; Repurposing; Data science; Field (mathematics); Prioritization; Key (lock); Knowledge extraction; Domain knowledge; Construct (python library); Domain (mathematical analysis); Pipeline (software); Perspective (graphical); Identification (biology); Drug; Artificial intelligence; Bioinformatics; Medicine; Management science; Pharmacology","retraction":null,"screen_n_in":null,"score":{"opus":0.05249995004114355,"gpt":0.3869085237927591,"spread":0.3344085737516156,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003236568,0.000527771,0.002046129,0.001155732,0.0001244804,0.0001632703,0.002699698,0.0001234045,0.00002856286],"category_scores_gemma":[0.002892608,0.0004775534,0.0005542796,0.004933504,0.0001229748,0.001186423,0.003415957,0.0008432748,0.00004327001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006912003,"about_ca_system_score_gemma":0.001605314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001638651,"about_ca_topic_score_gemma":0.000008340631,"domain_scores_codex":[0.9951815,0.0006101302,0.002292215,0.0005867061,0.0008586143,0.000470844],"domain_scores_gemma":[0.9949321,0.002349674,0.001239228,0.001193716,0.0001062211,0.0001791213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001434579,0.0001353531,1.32457e-7,0.1013997,0.00006744358,0.00001079418,0.00267001,0.00002986615,8.936596e-9,0.0349455,0.03511763,0.8256222],"study_design_scores_gemma":[0.0001241522,0.00004743238,7.036828e-7,0.09783715,0.00009766964,0.00009208333,0.00009892756,0.003895515,9.638693e-8,0.001449404,0.8959103,0.0004466335],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.289336e-7,0.90808,0.087294,0.0003749134,0.0003493917,0.00121188,0.0006162454,0.00006240413,0.00201107],"genre_scores_gemma":[6.083712e-8,0.8554309,0.1426553,0.001159011,0.0000334622,0.000153995,0.0005064791,0.00002793938,0.00003288424],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8607926,"threshold_uncertainty_score":0.9997676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2996038594","doi":"10.1093/bib/bbz124","title":"Current RNA-seq methodology reporting limits reproducibility","year":2019,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Computer science; Computational biology; Protocol (science); RNA-Seq; Data mining; Software; RNA; Raw data; Modular design; Data science; Biology; Gene; Transcriptome; Genetics; Medicine; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.2385703978866128,"gpt":0.4063465224701787,"spread":0.1677761245835658,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004109036,0.0005496758,0.001895829,0.0001570282,0.00007726058,0.00004553393,0.0004997784,0.0005271627,0.00000485916],"category_scores_gemma":[0.005735453,0.0004819632,0.0005653584,0.000245203,0.0001177295,0.000002215127,0.0006000903,0.0004689268,0.00003770584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000683629,"about_ca_system_score_gemma":0.0004785825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004212686,"about_ca_topic_score_gemma":0.00001897688,"domain_scores_codex":[0.9948368,0.0002067301,0.003180321,0.001051136,0.000184991,0.0005400323],"domain_scores_gemma":[0.9943115,0.0001455075,0.003208887,0.002125293,0.0001300257,0.00007880593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006413968,0.00003868271,0.0001062553,0.01182358,0.000122363,0.000002224335,0.0001517856,0.00001099278,0.00004541873,0.00005507881,0.001575883,0.9860613],"study_design_scores_gemma":[0.0001631722,0.00008658805,0.000077673,0.001708663,0.0002052273,0.00008835096,0.00002735521,0.00002424737,0.000116953,0.00010137,0.9968731,0.0005272359],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005164749,0.9958284,0.0004276705,0.00003773577,0.0009329694,0.0009209799,0.00005240954,0.0000095999,0.001273762],"genre_scores_gemma":[0.00002150684,0.9832484,0.0157695,0.0001755488,0.0002802737,0.00007316226,0.0002189645,0.00005473027,0.0001579737],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9952973,"threshold_uncertainty_score":0.9997632,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165086994","doi":"10.1093/bib/6.4.331","title":"Unsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data","year":2005,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Computer science; Annotation; Microarray databases; Artificial intelligence; Microarray analysis techniques; Data mining; Set (abstract data type); Consensus clustering; Data set; Gene chip analysis; Unsupervised learning; Machine learning; DNA microarray; Fuzzy clustering; Gene expression; CURE data clustering algorithm; Gene; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.07286423574329631,"gpt":0.3286876974485244,"spread":0.2558234617052281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003479475,0.0001985341,0.0003408881,0.00009994436,0.0000507148,0.00005387016,0.0004950694,0.000197603,0.00001328702],"category_scores_gemma":[0.00006160107,0.0001430965,0.00004722482,0.0001541423,0.00004809463,0.00002271835,0.0003493291,0.0001328519,0.000006150562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001956331,"about_ca_system_score_gemma":0.00008024839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002759512,"about_ca_topic_score_gemma":0.00025607,"domain_scores_codex":[0.998784,0.00005999139,0.0005995146,0.0002956066,0.000113968,0.0001468844],"domain_scores_gemma":[0.9986508,0.000008541645,0.0002939887,0.0009389595,0.00005437652,0.00005328763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006088075,0.00001568797,0.000002234057,0.001519078,0.00001619347,6.584354e-8,0.0002007357,0.000006669188,0.00009675699,7.386993e-7,0.006858738,0.991277],"study_design_scores_gemma":[0.0001354434,0.00005695459,0.00001363612,0.0008624393,0.0000501746,0.00002635417,0.0001212281,0.0004403768,0.0001075563,0.00000200352,0.9980018,0.0001820743],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002592374,0.9877825,0.008435939,0.0016887,0.0003092217,0.0009814786,0.0003530165,0.00001531798,0.0001745653],"genre_scores_gemma":[0.00006903918,0.9917521,0.004389097,0.0005221463,0.0006771301,0.00004381123,0.002471729,0.00002460287,0.00005032395],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.991143,"threshold_uncertainty_score":0.5835306,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2807480080","doi":"10.1093/bib/bby042","title":"Microbial genomic island discovery, visualization and analysis","year":2018,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Genome Canada","keywords":"Genome; Horizontal gene transfer; Genomics; Computational biology; Adaptation (eye); Biology; ENCODE; Comparative genomics; Genetics; Gene; Data science; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01539346263838015,"gpt":0.2837898113766908,"spread":0.2683963487383107,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000156211,0.0002701183,0.0007209981,0.0002252446,0.00009644768,0.0002676142,0.000196389,0.0002005457,0.0004418018],"category_scores_gemma":[0.00002165336,0.0002326597,0.00022169,0.0006609212,0.0001634121,0.0005330913,0.0003352041,0.000159146,0.0003086038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002344752,"about_ca_system_score_gemma":0.00002395311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005851736,"about_ca_topic_score_gemma":0.001246206,"domain_scores_codex":[0.9987015,0.00003207347,0.0007141961,0.0002280875,0.00008564844,0.0002385049],"domain_scores_gemma":[0.9992629,0.00003434435,0.0004166145,0.0002310357,0.000005683667,0.00004944774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001606928,0.0002001845,0.0003470891,0.008640987,0.001043743,0.0000109059,0.004634005,0.00003031775,0.0001183817,0.0001825743,0.03727018,0.9475055],"study_design_scores_gemma":[0.00009056687,0.00001870897,0.000354139,0.0007239448,0.0008072127,0.00004248373,0.00001532447,0.0006759593,0.000001921583,0.000006543646,0.9969736,0.0002895533],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002402489,0.9733928,0.01351873,0.000061962,0.0006174185,0.001656052,0.0008659285,0.0000901376,0.007394416],"genre_scores_gemma":[0.00003191375,0.9968184,0.001623802,0.0002157517,0.00004684281,0.00001144687,0.0004221681,0.00002334491,0.0008063144],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9597034,"threshold_uncertainty_score":0.9487588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4366551165","doi":"10.1093/bib/bbad131","title":"Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review","year":2023,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":97,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"CRISPR; Computer science; Artificial intelligence; Deep learning; Machine learning; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.07105972471653552,"gpt":0.4217742174339006,"spread":0.3507144927173651,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001050684,0.0003244297,0.0007784438,0.0002299153,0.0000850206,0.00003948806,0.00008888276,0.0002944609,0.000003692131],"category_scores_gemma":[0.0008125461,0.0003179573,0.00013474,0.0002210523,0.00004109935,0.000009974711,0.0001012877,0.0004471481,0.000001033473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004042413,"about_ca_system_score_gemma":0.00006070645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001158548,"about_ca_topic_score_gemma":0.000006668451,"domain_scores_codex":[0.998446,0.0001056531,0.0007816793,0.0002830017,0.0001021172,0.0002814783],"domain_scores_gemma":[0.9993318,0.0001996324,0.0002527321,0.0001193181,0.00002995252,0.00006656915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008272822,0.00001500826,0.00003170551,0.04580252,0.00006349976,0.000001753585,0.0001049994,0.0006895103,0.00001803042,0.00003652436,0.0001264658,0.9531017],"study_design_scores_gemma":[0.0003584273,0.0001690676,0.00001453669,0.01675273,0.0001449018,0.0001146963,0.00002973606,0.08672921,0.00001755139,0.00004082944,0.895291,0.0003372809],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001622663,0.939481,0.05953415,0.00002245542,0.0000759471,0.0007219934,0.00004885923,0.0000217179,0.00007767501],"genre_scores_gemma":[0.000003015871,0.916466,0.08251354,0.00011267,0.00007322161,0.0000963144,0.0006300831,0.00005206923,0.00005311273],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9527645,"threshold_uncertainty_score":0.9999272,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3095617312","doi":"10.1093/bib/bbaa266","title":"TrimNet: learning molecular representation from triplet messages for biomedicine","year":2020,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":97,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Biomedicine; Representation (politics); Graph; Feature learning; Artificial intelligence; Machine learning; Theoretical computer science; Property (philosophy); Bioinformatics","retraction":null,"screen_n_in":null,"score":{"opus":0.02323441463107265,"gpt":0.2815302091960457,"spread":0.258295794564973,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000812709,0.0001910924,0.0003626868,0.0001076806,0.0001276223,0.0002088066,0.0004563337,0.0000947552,0.0003058096],"category_scores_gemma":[0.002456318,0.000176908,0.00005799126,0.0004332042,0.0001327269,0.0004452318,0.0001439161,0.0001707639,0.0001223597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003827956,"about_ca_system_score_gemma":0.00005458524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003535737,"about_ca_topic_score_gemma":0.000004438255,"domain_scores_codex":[0.9980347,0.00009865627,0.0007553193,0.0003159087,0.0004255187,0.0003698862],"domain_scores_gemma":[0.9988615,0.0003227826,0.0004019048,0.0002190552,0.00006904403,0.0001257489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001858847,0.00003034308,0.001606731,0.000245081,0.000008698076,0.00001296252,0.007705974,0.01946256,0.9599948,0.0005897908,0.004819793,0.005337401],"study_design_scores_gemma":[0.003061261,0.0003996057,0.002187929,0.0001612472,0.00003519292,0.000007706637,0.0009710892,0.6847984,0.2744024,0.001224915,0.03219967,0.0005506176],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6442567,0.0001214505,0.3418512,0.01097989,0.0004142751,0.0007661223,0.00006792618,0.0003175971,0.001224889],"genre_scores_gemma":[0.6013407,0.00004105911,0.3888506,0.008978472,0.000275887,0.00009204814,0.0002471089,0.00005303552,0.0001211794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6855924,"threshold_uncertainty_score":0.72141,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2130666470","doi":"10.1093/bib/bbn042","title":"Gene-set analysis and reduction","year":2008,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":94,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Cancer Institute; Astellas Pharma; Genome Alberta; Canada Research Chairs; Muttart Foundation; University of Alberta; Fondation pour la Recherche Médicale; Roche Organ Transplant Research Foundation; Kidney Foundation of Canada; Canadian Institutes of Health Research; Genome Canada","keywords":"Microarray analysis techniques; Gene; Set (abstract data type); Computational biology; DNA microarray; Microarray; Microarray databases; Gene expression; Computer science; Gene chip analysis; Phenotype; Biology; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.01980434045847544,"gpt":0.2729433399758128,"spread":0.2531389995173373,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000216381,0.0004181479,0.001051928,0.0004250304,0.00009781757,0.00007053278,0.0002610104,0.000659823,0.000005910833],"category_scores_gemma":[0.00003408853,0.0003791315,0.0004109976,0.0006483425,0.0001380426,0.00001159841,0.0002438646,0.000295476,0.00001738378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004131378,"about_ca_system_score_gemma":0.0001577516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004021795,"about_ca_topic_score_gemma":0.00001760481,"domain_scores_codex":[0.9981073,0.00003456703,0.001079054,0.0002714341,0.0001517224,0.0003559332],"domain_scores_gemma":[0.9987605,0.0000166568,0.0005612857,0.000515201,0.00004131547,0.0001050848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005542567,0.00001660536,0.000009586775,0.00216848,0.0006255921,0.000002906746,0.0002077142,0.00003542317,0.000001527643,0.00003210991,0.006699867,0.9901946],"study_design_scores_gemma":[0.000184508,0.00004830469,0.00001009584,0.0004586642,0.000576282,0.000439132,0.0000308229,0.0008242371,0.00001107152,0.00002092359,0.9969122,0.0004837831],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008786441,0.9959384,0.002513757,0.000013523,0.0001234215,0.0004118283,0.00008616107,0.00001479438,0.000810239],"genre_scores_gemma":[0.0000174405,0.9884578,0.009439244,0.0001469938,0.0001596357,0.00003198162,0.001529273,0.00003202552,0.0001856146],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9902123,"threshold_uncertainty_score":0.9998661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3000373012","doi":"10.1093/bib/bbz164","title":"A survey and systematic assessment of computational methods for drug response prediction","year":2019,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Fonds National de la Recherche Luxembourg; National Natural Science Foundation of China","keywords":"Drug response; Computer science; Machine learning; Drug; Artificial intelligence; Personalized medicine; Data mining; Computational biology; Bioinformatics; Medicine; Biology; Pharmacology","retraction":null,"screen_n_in":null,"score":{"opus":0.02547935406031321,"gpt":0.364007076265487,"spread":0.3385277222051738,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009505783,0.0001290474,0.0003812914,0.0002924144,0.00004272633,0.0001061797,0.0003352412,0.00004805071,0.000001406559],"category_scores_gemma":[0.001039985,0.0001233469,0.00005379018,0.0004443131,0.00004057509,0.0006479571,0.0001996627,0.00009030844,0.000001652668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008835868,"about_ca_system_score_gemma":0.0002541435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002873239,"about_ca_topic_score_gemma":0.00000268226,"domain_scores_codex":[0.9976605,0.0008046324,0.0008765867,0.0001834427,0.0003075992,0.0001673062],"domain_scores_gemma":[0.9906908,0.008352858,0.0004137049,0.0002745163,0.0002231444,0.00004500175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003652402,0.0003612313,0.02860467,0.04251161,0.0002177163,0.000001326449,0.01479123,0.7384111,0.0002372931,0.1351121,0.0008009709,0.03858545],"study_design_scores_gemma":[0.0005635187,0.00007043578,0.120299,0.0006772046,0.000005138254,0.000009681356,0.00005396879,0.8715362,0.00003515352,0.006620092,0.00002896678,0.0001006545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08074751,0.00007278968,0.9177382,0.0001972358,0.000176285,0.0009206668,0.00003507663,0.00003392631,0.00007830694],"genre_scores_gemma":[0.1254108,0.000004536976,0.8742908,0.0002003848,0.000003613206,0.00003924366,0.00002091455,0.000006760069,0.00002298243],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1331251,"threshold_uncertainty_score":0.5029939,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3198604700","doi":"10.1093/bib/bbab360","title":"DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Subcellular localization; Subsequence; Embedding; Deep learning; Computer science; Artificial intelligence; Computational biology; Coding (social sciences); RNA; Longest common subsequence problem; Gene; Biology; Algorithm; Genetics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01087159442399887,"gpt":0.273727467343534,"spread":0.2628558729195351,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004995242,0.0002191878,0.0002012293,0.000163777,0.0002251948,0.0001274273,0.0001908125,0.0004020919,0.00001696837],"category_scores_gemma":[0.00131578,0.0002594602,0.0001227072,0.0004444181,0.00005277916,0.00002222072,0.000103229,0.0003886529,0.000006717687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001610081,"about_ca_system_score_gemma":0.0002122103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001573095,"about_ca_topic_score_gemma":0.00002257466,"domain_scores_codex":[0.9982592,0.00005905607,0.0004810031,0.0003502334,0.0003684611,0.0004820474],"domain_scores_gemma":[0.9990226,0.00007880861,0.0001789332,0.0003669472,0.0002498831,0.0001027954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000308216,0.000130617,0.0005134122,0.000791293,0.00007242062,0.00004854357,0.0007028453,0.8210992,0.1548544,0.002000438,0.0002443987,0.01923423],"study_design_scores_gemma":[0.000620613,0.0001831001,0.00005003171,0.0003138126,0.00001335468,0.00001401668,0.0001317627,0.6555217,0.3410506,0.0002057351,0.001706047,0.0001892416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00762002,0.0002173322,0.9910273,0.0001339642,0.0001714492,0.00043286,0.00001101086,0.00003627082,0.000349815],"genre_scores_gemma":[0.9051577,0.0003952369,0.0915764,0.001556645,0.0001679172,0.0001255257,0.0008369461,0.00007881367,0.0001047673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8994509,"threshold_uncertainty_score":0.9999858,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136539307","doi":"10.1093/bib/bbl025","title":"The Life Sciences Semantic Web is Full of Creeps!","year":2006,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":89,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Institute on Governance","funders":"Genome Alberta; Canadian Institutes of Health Research; Genome British Columbia; Government of Canada","keywords":"Semantic Web; World Wide Web; Social Semantic Web; Computer science; Web standards; Point (geometry); Semantic Web Stack; Data science; Web service","retraction":null,"screen_n_in":null,"score":{"opus":0.1952176454048163,"gpt":0.4176364411848498,"spread":0.2224187957800335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0146052,0.0003640301,0.001314093,0.0009798653,0.0005394755,0.001196124,0.004473242,0.000165063,0.00005186602],"category_scores_gemma":[0.005335034,0.0001985015,0.000449919,0.00411445,0.0008823602,0.0003901744,0.001451331,0.0003044005,0.000489611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004612806,"about_ca_system_score_gemma":0.0007014716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001442234,"about_ca_topic_score_gemma":0.00007981515,"domain_scores_codex":[0.9923102,0.0001759589,0.003637569,0.0005674135,0.002744635,0.0005642464],"domain_scores_gemma":[0.9918169,0.003741149,0.002349197,0.001796151,0.0002018452,0.00009477991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[8.190377e-7,0.0000168185,0.000005669267,0.0009836792,0.00001315031,9.323592e-7,0.0001110364,0.00002577193,5.566447e-9,0.001094845,0.2961109,0.7016364],"study_design_scores_gemma":[0.00008882418,0.00002975487,0.000008656409,0.002318452,0.00006989877,0.00001098921,0.0002598298,0.02419483,1.203153e-7,0.00124226,0.9715446,0.0002317747],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001195834,0.977631,0.00164905,0.0005594497,0.001027688,0.0005936846,0.0001284585,0.00003978362,0.01835892],"genre_scores_gemma":[0.00001257582,0.9926071,0.005081126,0.0004729859,0.00007123397,0.00001286548,0.00002259711,0.00001447223,0.001705046],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7014046,"threshold_uncertainty_score":0.9998407,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2561599208","doi":"10.1093/bib/bbw120","title":"Design of RNAs: comparing programs for inverse RNA folding","year":2016,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche","keywords":"Nucleic acid secondary structure; RNA; Computer science; Folding (DSP implementation); Computational biology; Preprocessor; Inverse; Synthetic biology; Theoretical computer science; Artificial intelligence; Biology; Mathematics; Engineering; Genetics; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.0747232420419325,"gpt":0.3053770490699766,"spread":0.2306538070280442,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007313786,0.0003707255,0.001060747,0.0001505931,0.00005066333,0.00003887499,0.0004662607,0.0004813036,0.000004220605],"category_scores_gemma":[0.0002297985,0.0002888931,0.000340855,0.0001314972,0.00007303841,0.00001141244,0.0001669123,0.00009813524,0.000009982288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003920401,"about_ca_system_score_gemma":0.0002052936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007114021,"about_ca_topic_score_gemma":0.00000252372,"domain_scores_codex":[0.9981077,0.00005019294,0.001063471,0.0002378358,0.0001567906,0.000384039],"domain_scores_gemma":[0.9984781,0.00009711043,0.0008580372,0.0004272896,0.00007305184,0.00006645984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002292625,0.00002855877,0.000001115696,0.01048096,0.00009860859,6.030344e-7,0.00006247927,0.000005719103,0.0006844601,0.0001719341,0.0006022272,0.9878404],"study_design_scores_gemma":[0.0004981544,0.0002221215,8.574712e-8,0.01588238,0.0001325396,0.00001942178,0.00002670634,0.0003714346,0.007428614,0.0002423673,0.9746815,0.0004946804],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001108256,0.7468228,0.2505137,0.000009614528,0.0001505166,0.002087312,0.00002583158,0.00001767182,0.0003614879],"genre_scores_gemma":[0.00001976298,0.9094713,0.08977578,0.00005488325,0.0000881728,0.0003156162,0.0001038165,0.00005544716,0.000115179],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9873458,"threshold_uncertainty_score":0.9999563,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3088070507","doi":"10.1093/bib/bbaa205","title":"DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method","year":2020,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Society of Canada; University of Calgary","funders":"Natural Science Foundation of Henan Province; Shanghai Jiao Tong University; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Drug target; Drug; Artificial intelligence; Machine learning; Pharmacology; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.07424663391965754,"gpt":0.3388700399352898,"spread":0.2646234060156323,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001338995,0.0002222246,0.0002685801,0.0002205934,0.0005426255,0.0003216463,0.0006153932,0.00005621744,0.000003289204],"category_scores_gemma":[0.0008385541,0.000222691,0.00005304713,0.0011427,0.00005276605,0.002102162,0.0005556692,0.001163985,0.00001208773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001657658,"about_ca_system_score_gemma":0.0001297439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007717169,"about_ca_topic_score_gemma":0.00009787463,"domain_scores_codex":[0.997892,0.0005964044,0.0006175564,0.0002082221,0.0003596302,0.0003261446],"domain_scores_gemma":[0.9978867,0.001155608,0.0003914019,0.0002880839,0.0001498282,0.0001283689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004145914,0.0001644626,0.004116029,0.0002846686,0.00005778313,0.000007948408,0.062349,0.817562,0.0008684915,0.0006211993,0.00002460387,0.1139023],"study_design_scores_gemma":[0.000717181,0.00007504777,0.001555931,0.0001345154,0.00001105783,0.00007295269,0.001604914,0.9929854,0.00193001,0.0002396158,0.0004244758,0.0002489294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1055008,0.00001375122,0.8930653,0.0004803344,0.0001260646,0.0002038053,0.000003297697,0.0002633925,0.0003432312],"genre_scores_gemma":[0.1866786,0.000001993746,0.8122289,0.001020875,0.00002937344,0.000006772521,0.000007479994,0.0000162522,0.000009818364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1754233,"threshold_uncertainty_score":0.9081078,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4200227922","doi":"10.1093/bib/bbab511","title":"Predicting drug–drug interactions by graph convolutional network with multi-kernel","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Graph; Drug; Kernel (algebra); Drug-drug interaction; Theoretical computer science; Medicine; Mathematics; Pharmacology","retraction":null,"screen_n_in":null,"score":{"opus":0.01413073872996588,"gpt":0.2631868213987384,"spread":0.2490560826687725,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005697515,0.0001990796,0.0002207084,0.0001192021,0.0002179245,0.0002923847,0.0004817662,0.00003140046,0.00001472269],"category_scores_gemma":[0.0002207708,0.00019739,0.00007476898,0.001054305,0.00009086324,0.001450848,0.0003809448,0.0003522682,0.00002163526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191691,"about_ca_system_score_gemma":0.0003325125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001646653,"about_ca_topic_score_gemma":0.0002278681,"domain_scores_codex":[0.9981428,0.0001145473,0.0006016013,0.0002766965,0.0004626004,0.0004016915],"domain_scores_gemma":[0.9982278,0.0008304621,0.0002652396,0.000349416,0.0002218613,0.0001052752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004803535,0.0006797332,0.04661186,0.0002761697,0.0002323197,0.0000873197,0.01346381,0.7170237,0.0000624266,0.0893691,0.1004582,0.03168744],"study_design_scores_gemma":[0.0006760669,0.000009295599,0.007126966,0.0001889627,0.000008185668,0.0001578236,0.0002585443,0.9804402,0.0001654821,0.002433856,0.008269221,0.0002654176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02567017,0.0002286177,0.9697857,0.002028508,0.0004220134,0.0001581666,0.00002458993,0.0001532205,0.001529048],"genre_scores_gemma":[0.06804289,0.00003232644,0.9284021,0.002769585,0.00007019157,0.00002930683,0.00008462116,0.00001714441,0.000551813],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2634165,"threshold_uncertainty_score":0.804933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110301462","doi":"10.1093/bib/bbs053","title":"Evaluation of research in biomedical ontologies","year":2012,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Association of Occupational Therapists; Carleton University","funders":"National Human Genome Research Institute","keywords":"Computer science; Ontology; Open Biomedical Ontologies; Terminology; Biomedicine; IDEF5; Data science; Consistency (knowledge bases); Controlled vocabulary; Domain (mathematical analysis); Information retrieval; Semantic Web; Upper ontology; Ontology alignment; Artificial intelligence; Bioinformatics","retraction":null,"screen_n_in":null,"score":{"opus":0.1457497501625115,"gpt":0.4224649152567926,"spread":0.2767151650942811,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006735094,0.0000826672,0.0001514583,0.0002457269,0.00002494507,0.000007842747,0.0001978016,0.0002637685,0.00001213739],"category_scores_gemma":[0.002229171,0.00007108516,0.00003055374,0.0003827781,0.0004381651,0.000009179535,0.0001518772,0.0001824063,0.000008280867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005044845,"about_ca_system_score_gemma":0.0001642976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001322254,"about_ca_topic_score_gemma":0.00006546977,"domain_scores_codex":[0.998179,0.0001738569,0.0004303403,0.00009805262,0.0007151317,0.0004036466],"domain_scores_gemma":[0.9993964,0.00008563573,0.00008209162,0.0002154231,0.0001607088,0.0000596697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001185693,0.0008264919,0.08101552,0.0003785261,0.00005403538,0.000002585225,0.005173848,0.00005629221,0.0106641,0.001007229,0.01312209,0.8875807],"study_design_scores_gemma":[0.01143028,0.002276121,0.5505826,0.001414109,0.0001064162,0.0001564713,0.02143929,0.03476217,0.08336408,0.007146527,0.2858112,0.001510736],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919325,0.001949756,0.0005101322,0.0005715783,0.0001613657,0.0002174133,0.000005705026,0.00001202441,0.004639541],"genre_scores_gemma":[0.9892923,0.000138842,0.01031925,0.0001104444,0.00006090214,0.00002539513,0.00002980774,0.00000542429,0.00001761538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.88607,"threshold_uncertainty_score":0.2898768,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4303646993","doi":"10.1093/bib/bbac430","title":"MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data","year":2022,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Institute of General Medical Sciences; National Center for Advancing Translational Sciences; National Eye Institute; National Heart, Lung, and Blood Institute","keywords":"Deconvolution; RNA-Seq; Computer science; Type (biology); Computational biology; Algorithm; Biology; Transcriptome; Gene; Genetics; Gene expression","retraction":null,"screen_n_in":null,"score":{"opus":0.05504419368871798,"gpt":0.3077461575687472,"spread":0.2527019638800292,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003050322,0.0001042211,0.00009577822,0.00006224513,0.0002144089,0.0000321612,0.0003856513,0.00006846086,0.00004584303],"category_scores_gemma":[0.00007122814,0.0001179749,0.00003635956,0.0001388648,0.00003738085,0.0000216365,0.000294884,0.00009267209,0.000007998289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005131251,"about_ca_system_score_gemma":0.0001298481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007644496,"about_ca_topic_score_gemma":0.00003511456,"domain_scores_codex":[0.9991504,0.00001678431,0.0003174913,0.0001995279,0.0001150999,0.0002007124],"domain_scores_gemma":[0.9991772,0.00001028788,0.0001635146,0.0005487921,0.0000643423,0.0000358289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006084804,0.001248404,0.001276425,0.000726476,0.0001741957,0.000003967985,0.002390962,0.01622568,0.3035129,0.003148158,0.5916979,0.07898653],"study_design_scores_gemma":[0.002255956,0.0003086315,0.0007439746,0.00001318907,0.00003474718,0.00002078365,0.0006192434,0.2092285,0.02030385,0.0001586603,0.7658876,0.0004248784],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.718097,0.002663233,0.2673576,0.001504575,0.001649063,0.002266255,0.002972897,0.00009130909,0.003398054],"genre_scores_gemma":[0.8959203,0.0004669392,0.08222716,0.004194681,0.0002196641,0.0003352822,0.0148189,0.00005095755,0.00176613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.283209,"threshold_uncertainty_score":0.4810878,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134975289","doi":"10.1093/bib/bbu044","title":"Comprehensive overview and assessment of computational prediction of microRNA targets in animals","year":2014,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computational biology; microRNA; Computer science; Identification (biology); Benchmark (surveying); Computational model; Biology; Gene; Artificial intelligence; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.03379539377691057,"gpt":0.3290256830012989,"spread":0.2952302892243883,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002496126,0.0002232972,0.000859162,0.0001973409,0.00001365944,0.00001013308,0.0001293536,0.0002674261,0.000005157417],"category_scores_gemma":[0.00004846254,0.0002211248,0.0001336287,0.0001636936,0.0001044997,0.00000754841,0.0001417047,0.000122936,5.970757e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004468424,"about_ca_system_score_gemma":0.0002281719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001938165,"about_ca_topic_score_gemma":0.000003898774,"domain_scores_codex":[0.9982234,0.00008238205,0.001220865,0.0001719764,0.0001636525,0.0001377244],"domain_scores_gemma":[0.998684,0.00005553231,0.0009107718,0.0002000254,0.000113882,0.00003576003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003656316,0.0002961199,0.001029204,0.2202573,0.0003938656,0.00000259367,0.0002763539,0.001460029,0.001371376,0.001199393,0.001647462,0.7720298],"study_design_scores_gemma":[0.001038452,0.0002055629,0.01202521,0.01345421,0.0001756055,0.00004706275,0.00002511181,0.006787105,0.0001780543,0.0002045113,0.9654763,0.0003828148],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001850914,0.9954019,0.001586782,0.000009271043,0.00004102141,0.0006308417,0.0003063487,0.000003841467,0.0001690808],"genre_scores_gemma":[0.001235798,0.9778509,0.01974043,0.00004708565,0.00002197119,0.00001916353,0.0010617,0.00001973809,0.000003227483],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9638289,"threshold_uncertainty_score":0.9017207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124920465","doi":"10.1093/bib/bbp064","title":"Simulation of P systems with active membranes on CUDA","year":2009,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"CUDA; Computer science; Key (lock); Class (philosophy); Central processing unit; Membrane computing; Parallel computing; Computational science; Simulation; Computer architecture; Computer hardware; Operating system; Theoretical computer science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01213112504075233,"gpt":0.2458033272438325,"spread":0.2336722022030802,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009746985,0.00009455544,0.0001293649,0.00003876332,0.00002586198,0.00001390088,0.00008942764,0.00009452985,0.000001014388],"category_scores_gemma":[0.0000873506,0.00006616077,0.00002698517,0.0000945897,0.00003305052,0.000005651057,0.00001899604,0.0000577402,0.000001748501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008147507,"about_ca_system_score_gemma":0.0000162232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001092701,"about_ca_topic_score_gemma":0.000001526757,"domain_scores_codex":[0.9994273,0.00001449804,0.0002381476,0.00009078042,0.00009905906,0.0001301688],"domain_scores_gemma":[0.9996215,0.00003441861,0.0001433592,0.000124068,0.00005306463,0.00002358695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009826735,0.000295311,0.001493153,0.0002907654,0.00005571101,0.000005188568,0.0007149181,0.8829049,0.03080109,0.001300738,0.0002268938,0.0809286],"study_design_scores_gemma":[0.00389133,0.008466813,0.02795498,0.0008346764,0.00003945495,0.00003645049,0.001090225,0.7240358,0.2115726,0.0003598126,0.02055036,0.001167496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896766,0.00005962591,0.004916633,0.00008213025,0.00003283614,0.0001821672,0.000005840257,0.00001250647,0.005031683],"genre_scores_gemma":[0.9982182,0.00001421386,0.001178281,0.0004921447,0.00003322072,0.000001300966,0.00003144543,0.000003326238,0.00002786072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1807715,"threshold_uncertainty_score":0.2697958,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3159261411","doi":"10.1093/bib/bbab165","title":"MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Society of Canada","funders":"National Natural Science Foundation of China","keywords":"Graph; Computer science; Topology (electrical circuits); Theoretical computer science; Mathematics; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.0165499375296665,"gpt":0.2650987480977511,"spread":0.2485488105680846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002775199,0.000217397,0.0001824377,0.00008878992,0.0003463905,0.0001077751,0.0001712385,0.0002480946,0.00001285533],"category_scores_gemma":[0.0003324811,0.0002019278,0.0001566535,0.0003769083,0.0002065818,0.00002729206,0.0001218649,0.0002410753,0.000002008709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004102303,"about_ca_system_score_gemma":0.0001325388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000357053,"about_ca_topic_score_gemma":0.00007066267,"domain_scores_codex":[0.9986561,0.0001058311,0.0003912564,0.0002731174,0.0002318643,0.00034185],"domain_scores_gemma":[0.9989818,0.0001212678,0.0002481134,0.000394383,0.0001571991,0.00009723075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001599096,0.00236602,0.1771667,0.00227764,0.002094581,0.0001096941,0.004731954,0.502107,0.06856142,0.0912692,0.124831,0.02288571],"study_design_scores_gemma":[0.006523062,0.0002641197,0.6033239,0.0007021065,0.0005330226,0.0001347458,0.001010095,0.2831416,0.007957356,0.0510833,0.04300926,0.002317471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2408688,0.007384971,0.7393047,0.008975118,0.0008742618,0.0009815244,0.0003766351,0.00009406971,0.001139956],"genre_scores_gemma":[0.9619371,0.000677907,0.02388567,0.01055285,0.0002567125,0.00005995758,0.002498463,0.0000443441,0.00008699254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7210683,"threshold_uncertainty_score":0.8234379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385373960","doi":"10.1093/bib/bbad289","title":"Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models","year":2023,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of General Medical Sciences; Bristol-Myers Squibb Canada; Michigan State University Foundation; Nuclear Safety and Security Commission; Bristol-Myers Squibb; National Aeronautics and Space Administration; National Institutes of Health; National Science Foundation; Michigan Economic Development Corporation; National Institute of Allergy and Infectious Diseases; Pfizer","keywords":"Computer science; Artificial intelligence; Topological data analysis; Protein engineering; Natural language processing; Computational biology; Biology; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.08793799256485617,"gpt":0.3524052727944185,"spread":0.2644672802295623,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009526258,0.000746408,0.001639092,0.0007406719,0.00008151499,0.0002254931,0.002148617,0.0009091995,0.00004377614],"category_scores_gemma":[0.001615425,0.0006641797,0.0004223166,0.001753305,0.00008070757,0.00004021627,0.002077955,0.0007920215,0.0002487048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009004574,"about_ca_system_score_gemma":0.0002142516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003676045,"about_ca_topic_score_gemma":0.0002973193,"domain_scores_codex":[0.9957688,0.00010477,0.002229529,0.000690665,0.0004946404,0.0007115305],"domain_scores_gemma":[0.9967106,0.00009972414,0.0006926263,0.002203423,0.00006822196,0.0002253884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005133208,0.0001736085,0.000003363954,0.01080147,0.002423106,0.00004831041,0.001787795,0.02152747,0.0000740876,0.002504821,0.0007155444,0.9598891],"study_design_scores_gemma":[0.0001324859,0.0002351787,0.000002907473,0.004113905,0.001344774,0.00001723632,0.0003927659,0.6442308,0.000241463,0.0006495393,0.3466512,0.001987748],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.001115596,0.4356805,0.5528654,0.0002650018,0.0002716141,0.006450762,0.002124841,0.0004419897,0.0007843237],"genre_scores_gemma":[0.0002772762,0.498168,0.47043,0.0006328632,0.0007419036,0.0009189324,0.02792231,0.0003110139,0.0005976589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9579014,"threshold_uncertainty_score":0.9995809,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3204096889","doi":"10.1093/bib/bbab395","title":"Prediction of RNA secondary structure including pseudoknots for long sequences","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Research Organization of Information and Systems","keywords":"Computational biology; Computer science; RNA; Biology; Genetics; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.02328085746481064,"gpt":0.2499489149499035,"spread":0.2266680574850928,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001920139,0.0001114746,0.0001585996,0.00004711936,0.00006034554,0.00002412554,0.000124327,0.0001774485,0.00002403129],"category_scores_gemma":[0.0002166519,0.0001104619,0.00006764914,0.0001012067,0.00004158063,0.00001634446,0.00008436345,0.0000671635,5.942853e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000136478,"about_ca_system_score_gemma":0.0001458096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001006638,"about_ca_topic_score_gemma":0.00002497337,"domain_scores_codex":[0.9991697,0.00001945641,0.0003895675,0.0001331368,0.0001162288,0.0001719292],"domain_scores_gemma":[0.9994621,0.00002520874,0.0001774799,0.000193722,0.000108536,0.00003295215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004017651,0.00001958116,0.0009108385,0.0004310089,0.0000403153,0.000002203079,0.0003898814,0.0001109575,0.9676597,0.0005154462,0.0009135446,0.0289664],"study_design_scores_gemma":[0.0004195848,0.00009835781,0.001014382,0.00009703427,0.00001173901,0.00003708775,0.000181433,0.0007893136,0.9914762,0.001382095,0.004375059,0.0001177109],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9512483,0.0007588498,0.04528951,0.0003055266,0.0002762807,0.000356181,0.0003063908,0.00001511509,0.001443819],"genre_scores_gemma":[0.9463141,0.0001766315,0.05242782,0.0005834418,0.00009412723,0.00001769883,0.000254961,0.00001480116,0.0001163997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02884869,"threshold_uncertainty_score":0.4504504,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226097900","doi":"10.1093/bib/bbac155","title":"RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation","year":2022,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Circular RNAs in diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Non-negative matrix factorization; Factorization; Computer science; Matrix decomposition; Matrix (chemical analysis); Mathematics; Artificial intelligence; Algorithm; Chemistry; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01325040169376098,"gpt":0.2458305388229346,"spread":0.2325801371291736,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003005669,0.0001444986,0.0001139869,0.0001250738,0.0003071103,0.00008180676,0.0001327626,0.00006198282,0.00001141097],"category_scores_gemma":[0.001051789,0.0001692631,0.00004151365,0.000241701,0.00004460589,0.00003098597,0.0001404385,0.0001475898,0.000001451549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001277292,"about_ca_system_score_gemma":0.0001544531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000261468,"about_ca_topic_score_gemma":0.000008533072,"domain_scores_codex":[0.9988483,0.00008515183,0.0003302689,0.0002012949,0.000335709,0.0001992722],"domain_scores_gemma":[0.9993097,0.00005453905,0.0002454807,0.0002264618,0.00007506886,0.00008873684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007044025,0.002037726,0.4773736,0.0009101758,0.0002132002,0.00002018993,0.003753127,0.4703674,0.01524043,0.006454024,0.005725674,0.01720005],"study_design_scores_gemma":[0.002889922,0.0004488667,0.1065348,0.0000888213,0.00007595832,0.000006217628,0.0007003537,0.882467,0.002496972,0.0004726291,0.003194396,0.0006240291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8943263,0.0002463627,0.09951881,0.00111851,0.0003370113,0.001416318,0.001140002,0.0001133883,0.001783269],"genre_scores_gemma":[0.9940606,0.00001671201,0.003399827,0.001012273,0.00006015263,0.00009413059,0.001260288,0.00002374844,0.00007227273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4120997,"threshold_uncertainty_score":0.6902348,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3123897428","doi":"10.1093/bib/bbaa403","title":"Oxford nanopore sequencing in clinical microbiology and infection diagnostics","year":2020,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":72,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Workflow; Nanopore sequencing; Turnaround time; Context (archaeology); Point of care; Computer science; Identification (biology); Clinical microbiology; Point-of-care testing; Software; Molecular diagnostics; Data science; DNA sequencing; Medicine; Bioinformatics; Biology; Microbiology; Database; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.03387400978782597,"gpt":0.2822925424533028,"spread":0.2484185326654769,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003427766,0.00009967954,0.000156756,0.00004016358,0.00003242005,0.00003735465,0.00007517372,0.0001899489,0.000008041786],"category_scores_gemma":[0.001804014,0.0001062291,0.00003338342,0.0001245685,0.00008625349,0.00001237563,0.0001133272,0.0001519113,0.000006193066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002476264,"about_ca_system_score_gemma":0.00004892655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006617347,"about_ca_topic_score_gemma":0.0002297725,"domain_scores_codex":[0.9989592,0.00004935222,0.0006210979,0.0001801071,0.00002821517,0.0001620239],"domain_scores_gemma":[0.9995877,0.00004984278,0.0001372502,0.0001249146,0.0000368066,0.0000634932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008035846,0.00009308045,0.7647367,0.0004077798,0.00002534501,0.000007912073,0.002897232,0.0001156275,0.19427,0.000291927,0.004897896,0.03217613],"study_design_scores_gemma":[0.007644461,0.001800957,0.5266861,0.0004676673,0.00006127753,0.0002826085,0.001642996,0.08940928,0.03880471,0.0004088006,0.3306298,0.002161386],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980462,0.00008353695,0.0005671068,0.0007190517,0.000103609,0.00015153,0.000008770066,0.00001563627,0.0003045163],"genre_scores_gemma":[0.9905277,0.0009908149,0.004472462,0.00378434,0.00008578374,0.00000570648,0.0001075233,0.000009570936,0.00001607057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3257319,"threshold_uncertainty_score":0.4331898,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156545100","doi":"10.1093/bib/bbt043","title":"Best practices in bioinformatics training for life scientists","year":2013,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences; Novo Nordisk Fonden; Sveriges Lantbruksuniversitet; Wellcome Trust; King Abdullah University of Science and Technology; European Commission; Uppsala Universitet; Ontario Institute for Cancer Research; European Bioinformatics Institute","keywords":"Excellence; Training (meteorology); Context (archaeology); Computer science; Interactivity; Resource (disambiguation); Quality (philosophy); Globe; Data science; Knowledge management; Multimedia; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.06911075359891737,"gpt":0.3381606877965708,"spread":0.2690499341976534,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00140829,0.0003389229,0.0004046609,0.0004621537,0.0001524969,0.0003529042,0.000710753,0.0004113193,0.00004488221],"category_scores_gemma":[0.004925893,0.0003130149,0.0001347182,0.0005248937,0.0003792494,0.0001339609,0.000335242,0.0003188996,0.0001750216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006463831,"about_ca_system_score_gemma":0.0005436001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003517178,"about_ca_topic_score_gemma":0.0002924516,"domain_scores_codex":[0.9965566,0.00003682064,0.001463261,0.0002853135,0.000598333,0.001059712],"domain_scores_gemma":[0.9979324,0.0001637227,0.0007121962,0.000515855,0.0002917412,0.0003840753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005497675,0.001594297,0.0155448,0.006763674,0.0003126227,0.00001445461,0.02650717,0.0009174831,0.01881215,0.001618126,0.1380022,0.7893633],"study_design_scores_gemma":[0.01107494,0.0026782,0.01269853,0.001020893,0.00006737067,0.0001357969,0.02709114,0.4876779,0.01190911,0.001882349,0.4409558,0.002808035],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145586,0.0009375957,0.03668776,0.006523349,0.001223649,0.005799409,0.00022466,0.000102728,0.03394228],"genre_scores_gemma":[0.6026211,0.002268508,0.379552,0.01033682,0.0006041078,0.0007206019,0.001024618,0.0001228843,0.002749405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7865552,"threshold_uncertainty_score":0.9999322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2140743149","doi":"10.1093/bib/bbp058","title":"Genome variation discovery with high-throughput sequencing data","year":2010,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Genome; DNA sequencing; Computer science; Computational biology; Structural variation; Identification (biology); Genomics; Throughput; Human genome; Biology; Genetics; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.01429686147796803,"gpt":0.2234970998298424,"spread":0.2092002383518744,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002639579,0.0001513969,0.0001416173,0.00004145598,0.00008268168,0.00008227231,0.0003747343,0.0001101653,0.000003632521],"category_scores_gemma":[0.00007873758,0.0001299677,0.00001922791,0.0000973337,0.00008360315,0.00001347798,0.0003766303,0.0001374056,0.000005845087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000157502,"about_ca_system_score_gemma":0.0001324639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000267899,"about_ca_topic_score_gemma":0.0004650381,"domain_scores_codex":[0.9991124,0.000009597971,0.0002894759,0.0002300649,0.0001205174,0.0002378998],"domain_scores_gemma":[0.9990343,0.00001410588,0.0001368633,0.000729205,0.00004774322,0.00003781299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006827204,0.00005286673,0.005221651,0.00009595821,0.0001168118,0.000004753268,0.001461453,0.0009459589,0.9875616,0.002470967,0.0004188297,0.001580894],"study_design_scores_gemma":[0.006998855,0.001383071,0.7057022,0.0001562018,0.0002344623,0.0004433638,0.001859205,0.01758244,0.09505666,0.00373403,0.1634051,0.003444344],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888222,0.00008387932,0.009411574,0.0002226701,0.0001571454,0.0001745051,0.0001040114,0.000005762628,0.001018312],"genre_scores_gemma":[0.9420007,0.0001287926,0.05673005,0.00059499,0.000156323,0.000008152917,0.0002927717,0.00001765694,0.00007055124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8925049,"threshold_uncertainty_score":0.5299927,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2140466892","doi":"10.1093/bib/bbt007","title":"The Rat Genome Database 2013--data, tools and users","year":2013,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Smiths Detection (Canada)","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Genomics; Genome; Focus (optics); Computer science; World Wide Web; Translational research; Computational biology; Biology; Data science; Genetics; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.02489016400744046,"gpt":0.2544243681694289,"spread":0.2295342041619885,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002228951,0.00009659713,0.00007039362,0.00002806953,0.0001178858,0.0001732625,0.0003463733,0.00007040089,0.00001491105],"category_scores_gemma":[0.0001158414,0.00006994027,0.00001477891,0.00007352323,0.00009192478,0.00004274058,0.0003206294,0.00007526268,0.00003551607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008408838,"about_ca_system_score_gemma":0.00004413948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006363987,"about_ca_topic_score_gemma":0.00003878075,"domain_scores_codex":[0.9992886,0.00001833322,0.0002551361,0.0001509949,0.000102738,0.0001841544],"domain_scores_gemma":[0.9991168,0.00002155644,0.00009893377,0.0006599255,0.00004240492,0.00006037886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000480249,0.00005344547,0.002040834,0.0001212616,0.00004003875,6.929683e-7,0.0005451587,0.00004975047,0.3150449,0.0007790819,0.5779411,0.1033358],"study_design_scores_gemma":[0.000491179,0.00003963436,0.009679042,0.00002126004,0.000006252227,0.000009889771,0.0004979852,0.004389883,0.006813861,0.00009541855,0.9777458,0.00020978],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9356312,0.007647433,0.01904075,0.02305102,0.0009321877,0.002565026,0.0004207328,0.00008663729,0.01062508],"genre_scores_gemma":[0.9022477,0.02954259,0.039929,0.01726928,0.0005059701,0.0003690291,0.00507742,0.00009186745,0.004967106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3998047,"threshold_uncertainty_score":0.2852081,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}