{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":70,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":70,"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":"a193daa9e1d4","filters":{"venue":"Computational Molecular Biology"}},"results":[{"id":"W2111105704","doi":"10.5376/cmb.2014.04.0007","title":"FunSecKB2: a fungal protein subcellular location knowledgebase","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computational biology; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.00442848962742088,"gpt":0.2555454032499011,"spread":0.2511169136224802,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003413369,0.0002092295,0.0001658544,0.00009046942,0.0001052872,0.00002587262,0.0002605115,0.0002138888,0.00003306006],"category_scores_gemma":[0.0004492998,0.0002122104,0.00008977421,0.0001567426,0.0001414106,0.000004031608,0.0001389364,0.0001546149,0.0001521324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000178978,"about_ca_system_score_gemma":0.000109042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005649483,"about_ca_topic_score_gemma":0.000003354469,"domain_scores_codex":[0.9986374,0.0002754767,0.0003175046,0.0003641355,0.0001282342,0.0002772684],"domain_scores_gemma":[0.999106,0.00003175421,0.000146983,0.0003336715,0.0002801516,0.0001014555],"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":[0.0001508583,0.0001969084,0.004585494,0.0001411308,0.0001487634,0.000007246499,0.00008791754,0.05431144,0.7172076,0.2082155,0.001104636,0.0138425],"study_design_scores_gemma":[0.005903393,0.004355034,0.008934918,0.0001397571,0.0001184053,0.0003682518,0.00006318663,0.4439774,0.1430698,0.2060022,0.1843792,0.002688435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2978359,0.0002439034,0.6991056,0.0002065225,0.0001112807,0.0002229238,0.000008126481,0.00003463315,0.002231215],"genre_scores_gemma":[0.9699801,0.000001311896,0.02744657,0.000670138,0.0001943658,0.00004567635,0.00137721,0.00002920689,0.0002553914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6721443,"threshold_uncertainty_score":0.8653691,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1935810336","doi":"10.5376/cmb.2014.04.0001","title":"PlantSecKB: the Plant Secretome and Subcellular Proteome KnowledgeBase","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Biochemical and Structural Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Proteome; Computational biology; Subcellular localization; Biology; Bioinformatics; Biochemistry; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.006134736165057712,"gpt":0.2217877901096197,"spread":0.215653053944562,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001187315,0.0001406341,0.0001140052,0.00002639484,0.00009862005,0.00002039071,0.0001476568,0.0001426372,0.00001199275],"category_scores_gemma":[0.00008341423,0.000098189,0.00004535603,0.00005781343,0.0001746737,0.000002198656,0.0001148869,0.00008937789,0.000008916222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004720667,"about_ca_system_score_gemma":0.00002327104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002894577,"about_ca_topic_score_gemma":0.000001168555,"domain_scores_codex":[0.9991664,0.0001287821,0.000157523,0.000309577,0.00006648213,0.0001712741],"domain_scores_gemma":[0.9996209,0.00003078129,0.00006338648,0.0001525447,0.00006588318,0.00006656082],"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":[0.00005239658,0.00001190223,0.0007577166,0.00001368393,0.00003073986,0.000001674338,0.00001654821,0.0001365088,0.9833298,0.01432329,0.00007972575,0.001246026],"study_design_scores_gemma":[0.002513611,0.001361879,0.03022619,0.00004234054,0.00007407625,0.0005792263,0.00003286874,0.02954314,0.6906299,0.1968466,0.04687265,0.001277509],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8802987,0.0003278974,0.1183968,0.0004826485,0.00009909031,0.0001747524,0.00005230739,0.00001335023,0.0001544671],"genre_scores_gemma":[0.9960098,0.000009323432,0.0009570148,0.0007647669,0.0001817476,0.00002134585,0.002000828,0.00001214947,0.00004303719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2926999,"threshold_uncertainty_score":0.4004031,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097411884","doi":"10.5376/cmb.2012.02.0001","title":"Computational Prediction of Protein Subcellular Locations in Eukaryotes: an Experience Report","year":2012,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Signal peptide; Endoplasmic reticulum; Proteome; Transmembrane protein; Computational biology; Subcellular localization; Secretory protein; Protein targeting; Secretory pathway; Protein Sorting Signals; Computational model; Computer science; Membrane protein; Biology; Cell biology; Bioinformatics; Biochemistry; Secretion; Peptide sequence; Golgi apparatus; Artificial intelligence; Membrane; Gene; Cytoplasm","retraction":null,"screen_n_in":null,"score":{"opus":0.01228365564142645,"gpt":0.2999240799302989,"spread":0.2876404242888724,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390386,0.0001465488,0.0001540925,0.0001300429,0.00005303361,0.000007896431,0.0001816819,0.0001619737,0.00002017175],"category_scores_gemma":[0.0002219044,0.0001571723,0.00005684623,0.0001696425,0.0001789122,0.00001361021,0.0000858198,0.000115705,0.000006645433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002390404,"about_ca_system_score_gemma":0.0001168001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002205007,"about_ca_topic_score_gemma":0.000002353576,"domain_scores_codex":[0.9986629,0.0001705924,0.000518344,0.0002475317,0.0001618706,0.0002387333],"domain_scores_gemma":[0.9992211,0.00002498121,0.0002253072,0.0002559769,0.0001830348,0.00008960976],"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":[0.00006710752,0.0005115247,0.3102756,0.00005872954,0.00006459837,0.00001048765,0.0007359671,0.4190045,0.2318205,0.03631828,0.00004575614,0.001086907],"study_design_scores_gemma":[0.003281172,0.00197354,0.4681677,0.0001214493,0.00005391611,0.001082099,0.0006265351,0.3976826,0.05452541,0.06665672,0.004511581,0.001317197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6064494,0.0001698618,0.392803,0.00004444463,0.0000706038,0.0001883527,0.00002526686,0.00001216334,0.0002368689],"genre_scores_gemma":[0.9438656,0.000001112334,0.05258024,0.0001146454,0.00007480443,0.00005307151,0.003269686,0.00001564571,0.00002516608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3402228,"threshold_uncertainty_score":0.6409299,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4412354158","doi":"10.5376/cmb.2024.14.0024","title":"Emerging Trends in Systems Biology: Multi-Omics Integration and Beyond","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Systems biology; Omics; Data science; Biology; Computational biology; Computer science; Bioinformatics","retraction":null,"screen_n_in":null,"score":{"opus":0.009088482904857196,"gpt":0.2841175448171122,"spread":0.275029061912255,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001973854,0.000153804,0.0001490218,0.0002130753,0.00004093571,0.00004801262,0.00009817107,0.0002266626,0.00000436312],"category_scores_gemma":[0.00001653673,0.0001431948,0.00005470383,0.0001649542,0.0001024932,0.000004785314,0.0000945162,0.0001320111,0.000005726696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001958722,"about_ca_system_score_gemma":0.00004130127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001582018,"about_ca_topic_score_gemma":0.0000125328,"domain_scores_codex":[0.999079,0.00007185354,0.0002870403,0.0003264338,0.00003515267,0.000200499],"domain_scores_gemma":[0.9997264,0.00002248297,0.00004644414,0.0001146572,0.00004140665,0.00004859365],"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":[0.00005459357,0.00007104203,0.001649431,0.00007055791,0.0002224906,0.00002153919,0.0003263972,0.03214321,0.2621233,0.4675234,0.0008689061,0.2349251],"study_design_scores_gemma":[0.001063012,0.0003777841,0.001554861,0.00005211559,0.0000303653,0.0001283743,0.0001644128,0.8999187,0.001150406,0.08435398,0.010639,0.0005670341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1188336,0.008652509,0.8708502,0.0003470684,0.0005355413,0.0001289503,0.00005521303,0.00002320049,0.0005737304],"genre_scores_gemma":[0.9893752,0.00006512187,0.00833944,0.0002852922,0.0001020677,0.00002118357,0.001717314,0.0000170409,0.00007734649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8705416,"threshold_uncertainty_score":0.5839316,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2750587080","doi":"10.5376/cmb.2017.07.0001","title":"Comprehensive Cataloging and Analysis of Alternative Splicing in Maize","year":2017,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Cataloging; Biotechnology; Biology; Computational biology; Geography; Botany; Computer science; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.02000340360077993,"gpt":0.3462154719110377,"spread":0.3262120683102578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001120785,0.00008989142,0.0002098763,0.0001892803,0.00008430561,0.00001890455,0.000200395,0.00006802035,0.000002908758],"category_scores_gemma":[0.0001305605,0.00008904303,0.00006662262,0.00008637908,0.000226756,0.000003662749,0.0002324583,0.00006484359,7.260707e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008758376,"about_ca_system_score_gemma":0.00003229222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002804377,"about_ca_topic_score_gemma":0.00005808228,"domain_scores_codex":[0.9992495,0.00007263568,0.000168814,0.00027772,0.00006864462,0.0001626963],"domain_scores_gemma":[0.9994514,0.00004132118,0.0001243632,0.0002291305,0.0001082673,0.00004552368],"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":[0.000056766,0.00002450768,0.1150733,0.00001264743,0.0004728328,0.00001854942,0.00003549075,0.0145093,0.8590012,0.007411066,0.000005312264,0.003379064],"study_design_scores_gemma":[0.001238737,0.0002787061,0.8896661,0.00002381585,0.00009084825,0.00001408618,0.00006789436,0.05691699,0.03555373,0.01578113,0.0001049117,0.0002630008],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9327996,0.000340494,0.06644694,0.0001110463,0.00002410364,0.00008227619,0.00002729421,0.000001944637,0.0001663502],"genre_scores_gemma":[0.9968408,0.00002687821,0.002698927,0.0001095233,0.00001577305,0.000005963967,0.0002772402,0.000006177151,0.00001876546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8234474,"threshold_uncertainty_score":0.3631069,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406457227","doi":"10.5376/cmb.2024.14.0020","title":"Bioinformatics in the Age of Big Data: Leveraging Computational Tools for Biological Discoveries","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Big data; Data science; Computer science; Computational biology; Biology; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.09336499182371469,"gpt":0.3546909107620051,"spread":0.2613259189382904,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007281429,0.0001604274,0.0001979109,0.0001334362,0.00006357258,0.0001051133,0.0006653671,0.0001686607,0.000004411918],"category_scores_gemma":[0.0006428898,0.0001097768,0.0001046334,0.0002084597,0.000486376,0.00001489801,0.0003367252,0.0001316688,0.000006110022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000013274,"about_ca_system_score_gemma":0.0002533825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006547282,"about_ca_topic_score_gemma":0.000006185961,"domain_scores_codex":[0.9985561,0.0001135178,0.000508883,0.0003117348,0.0002226284,0.0002871856],"domain_scores_gemma":[0.9989951,0.0004680333,0.00006951059,0.0003053072,0.0001190043,0.00004310276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000743058,0.0008988319,0.004187016,0.002077887,0.001207167,0.0001273477,0.003062178,0.0702531,0.1603589,0.2382212,0.01650066,0.5023627],"study_design_scores_gemma":[0.003239255,0.003130029,0.0120367,0.0002452751,0.00008800571,0.0002492373,0.001935546,0.3500403,0.003791087,0.4491465,0.1748426,0.001255407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07879026,0.0009656406,0.9177302,0.0009003112,0.0003087284,0.0004275448,0.0005385956,0.00001134523,0.0003274053],"genre_scores_gemma":[0.946601,0.00007675988,0.03970372,0.0007901515,0.0001874924,0.00003503107,0.01257956,0.00001139581,0.00001486708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8780264,"threshold_uncertainty_score":0.4476567,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859858","doi":"10.5376/cmb.2024.14.0008","title":"Emerging Trends in Multi-Omics Data Integration: Challenges and Future Directions","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Omics; Data science; Computational biology; Data integration; Computer science; Biology; Data mining; Bioinformatics","retraction":null,"screen_n_in":null,"score":{"opus":0.02640672786139943,"gpt":0.3123454971918981,"spread":0.2859387693304987,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001550704,0.0001233012,0.0001064416,0.0001407796,0.000040977,0.00002855776,0.0001585725,0.0001574038,0.000007157462],"category_scores_gemma":[0.00001045756,0.0001162503,0.00003372417,0.0001163622,0.00006453796,0.000007151194,0.0001946816,0.0001240614,0.000003509262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001015039,"about_ca_system_score_gemma":0.00003592509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004363097,"about_ca_topic_score_gemma":0.00005776828,"domain_scores_codex":[0.999219,0.00004709026,0.0001933822,0.0003584662,0.00003837253,0.0001436735],"domain_scores_gemma":[0.9996625,0.00001578457,0.00002863848,0.0002232397,0.00002956176,0.00004027034],"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":[0.00001310782,0.00003084639,0.0000387982,0.0000199451,0.00008050767,0.000008445953,0.0002448624,0.0007832827,0.00556814,0.06693464,0.0003656493,0.9259118],"study_design_scores_gemma":[0.001058087,0.0002720286,0.006143413,0.00005787786,0.00004847664,0.0002283403,0.0004735976,0.503018,0.0003193913,0.06989266,0.4177868,0.0007014314],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02824816,0.1893632,0.771049,0.007572063,0.001383222,0.0002119658,0.0002084704,0.00006427848,0.001899628],"genre_scores_gemma":[0.9665626,0.004622182,0.02362518,0.0003360331,0.0004517443,0.00001586114,0.004288434,0.0000226273,0.000075378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9383144,"threshold_uncertainty_score":0.474055,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859874","doi":"10.5376/cmb.2024.14.0014","title":"Exploration of the Role of Computational Chemistry in Modern Drug Discovery","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Drug discovery; Chemistry; Drug; Computational biology; Pharmacology; Biochemistry; Medicine; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01244006676505606,"gpt":0.294870183796754,"spread":0.282430117031698,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003460982,0.0001480473,0.0002145446,0.0001560778,0.00003341796,0.00005185706,0.0006674642,0.00006648171,0.000004353069],"category_scores_gemma":[0.0000855689,0.000126432,0.0001532094,0.0007315379,0.0001712756,0.0005012073,0.0003553775,0.0001593018,0.000002758171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000619514,"about_ca_system_score_gemma":0.0004380269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000203534,"about_ca_topic_score_gemma":0.000002064177,"domain_scores_codex":[0.9983267,0.0002849212,0.0004972407,0.0004078604,0.000325771,0.0001575563],"domain_scores_gemma":[0.998775,0.0006360047,0.0001469819,0.0002529068,0.0001598153,0.00002926052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004857581,0.00004357411,0.0002271389,0.00003331618,0.00002164964,0.000002312018,0.0002992605,0.6314923,0.009006121,0.3499786,0.000006970963,0.008883866],"study_design_scores_gemma":[0.00009134303,0.000008064208,0.001088833,0.00003725615,0.000003074504,0.000006550582,0.00001702943,0.4652089,0.005310005,0.5281398,0.00002501319,0.00006413312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.210449,0.0009485294,0.7872198,0.0006060653,0.0001830542,0.0001354222,0.00002641363,0.00002945902,0.0004022296],"genre_scores_gemma":[0.9498432,0.000002081267,0.04994588,0.00007228309,0.00002274568,0.00001771183,0.00006594226,0.00001075437,0.00001935714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7393942,"threshold_uncertainty_score":0.5155749,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1946690017","doi":"10.5376/cmb.2014.04.0006","title":"Phylogeny in Few Species of Leguminosae Family Based on <i>matK</i> Sequence","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Ethnobotanical and Medicinal Plants Studies","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Phylogenetics; Sequence (biology); Biology; Evolutionary biology; Computational biology; Genetics; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.02887293030852661,"gpt":0.2582998432646054,"spread":0.2294269129560788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002496872,0.0001588504,0.0003063733,0.00003125284,0.000072898,0.000006759696,0.0002316946,0.0001135229,0.00004007059],"category_scores_gemma":[0.0002348092,0.00007003651,0.00009022957,0.0002462896,0.0002956358,0.00001727294,0.0000635932,0.000109143,0.00002766448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053267,"about_ca_system_score_gemma":0.00001709883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002621741,"about_ca_topic_score_gemma":0.0001012129,"domain_scores_codex":[0.9987314,0.0001911353,0.0002958649,0.0003260935,0.0002002899,0.000255192],"domain_scores_gemma":[0.9988652,0.0008019544,0.0001048979,0.00005695803,0.0001025149,0.00006849146],"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":[0.00004595286,0.0001145415,0.001425577,0.00001072792,0.00001162021,0.00001041354,0.00001491243,0.002118631,0.9321695,0.05577215,0.0001443506,0.00816161],"study_design_scores_gemma":[0.0009768668,0.002732023,0.8503799,0.0001839774,0.00002107833,0.00001173012,0.00006135296,0.01062986,0.00667201,0.1075879,0.02018621,0.0005571298],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907545,0.0002978516,0.002462459,0.001699773,0.0001286448,0.0001450298,0.00006028527,0.00002530142,0.004426129],"genre_scores_gemma":[0.9972104,0.00001683847,0.0007031518,0.001779408,0.00008775096,0.00001022762,0.0001497228,0.000001146831,0.00004138794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9254975,"threshold_uncertainty_score":0.2856006,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405860078","doi":"10.5376/cmb.2024.14.0013","title":"AI in Biology: Transforming Genomic Research with Machine Learning","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Biology; Computational biology; Cognitive science; Evolutionary biology; Data science; Computer science; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02516239825888492,"gpt":0.3662489068911247,"spread":0.3410865086322398,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009249844,0.0001836412,0.0001871933,0.0004125068,0.0001094581,0.00005712626,0.0003032403,0.0002737301,0.00002578279],"category_scores_gemma":[0.0001481119,0.0001484373,0.00007634675,0.0003975708,0.0005143481,0.00000545056,0.0001697737,0.000595563,0.00005901897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005298788,"about_ca_system_score_gemma":0.0003874437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005241041,"about_ca_topic_score_gemma":0.0000600204,"domain_scores_codex":[0.9980906,0.000296773,0.0003268422,0.0004985163,0.000216326,0.0005709504],"domain_scores_gemma":[0.9993858,0.000108629,0.00002551662,0.0001671921,0.0001811092,0.0001317791],"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":[0.0002421078,0.0001087116,0.005525977,0.0001804537,0.0002041094,0.00009575883,0.0002560099,0.005151339,0.9079066,0.0259168,0.0003368433,0.05407529],"study_design_scores_gemma":[0.005510997,0.0111022,0.007920895,0.000454123,0.00006651861,0.0005424064,0.0005186819,0.2308875,0.07526398,0.2829804,0.3825275,0.002224761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6087426,0.006186434,0.3800506,0.002729238,0.0001963481,0.0004917118,0.00004850299,0.0000460651,0.001508518],"genre_scores_gemma":[0.9935738,0.0002119853,0.004338314,0.0004586011,0.0001212802,0.00003895511,0.001082011,0.00002969624,0.0001453562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8326426,"threshold_uncertainty_score":0.6053098,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1826391132","doi":"10.5376/cmb.2015.05.0004","title":"Changes of Molecular, Cellular and Biological Activities According to microRNA-mRNA Interactions in Ovarian Cancer","year":2015,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"microRNA; Ovarian cancer; Messenger RNA; Cancer; Biology; Computational biology; Cancer research; Gene; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.02939411871085877,"gpt":0.3147864472283358,"spread":0.285392328517477,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001474322,0.0001640689,0.0002078748,0.0001866704,0.00002680977,0.00001257985,0.0001361321,0.0001165273,0.000007634389],"category_scores_gemma":[0.000116877,0.0001677157,0.0000518136,0.0001683529,0.0001216387,0.000005122266,0.0002038082,0.00007796812,0.000002060395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004331006,"about_ca_system_score_gemma":0.0001126349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006964404,"about_ca_topic_score_gemma":0.00005192805,"domain_scores_codex":[0.9989399,0.0001628751,0.0002235866,0.0003874151,0.00007857346,0.0002076124],"domain_scores_gemma":[0.9994559,0.00002911108,0.0001063304,0.0001555394,0.0001285065,0.0001246315],"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":[0.0001089069,0.00005044567,0.01183221,0.0000110521,0.00005310196,0.000007995601,0.00005934983,0.005437962,0.9791899,0.002009052,0.00004709961,0.001192976],"study_design_scores_gemma":[0.001191476,0.0003744923,0.01794486,0.0000575064,0.00002628482,0.0000374776,0.000265822,0.0006644982,0.9653093,0.01240897,0.001301857,0.000417461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130251,0.001144667,0.08466574,0.0006559226,0.0001385213,0.0002287273,0.00004750603,0.000007951464,0.00008585679],"genre_scores_gemma":[0.9930094,0.00001531845,0.006163325,0.00041069,0.0000625167,0.00005794441,0.0002531646,0.00001736773,0.00001021067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07998435,"threshold_uncertainty_score":0.6839249,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2143437919","doi":"10.5376/cmb.2014.04.0013","title":"In-silico analysis predicting the best model for photosystemIID2 Protein of Spinaciaolearacea using multiple templates","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"In silico; UniProt; Template; Spinach; Computational biology; Protein structure prediction; Computer science; Chemistry; Bioinformatics; Protein structure; Biology; Biochemistry; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.0187321794491058,"gpt":0.3004691566742135,"spread":0.2817369772251077,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003679839,0.0001391571,0.0002961686,0.000147062,0.00008842113,0.000008492262,0.0001764429,0.0001095267,0.000001177802],"category_scores_gemma":[0.0003217202,0.0001118657,0.0001738196,0.0002580144,0.0001036737,0.000002698678,0.0001059163,0.00006005,4.381705e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001252226,"about_ca_system_score_gemma":0.00004851393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004353663,"about_ca_topic_score_gemma":0.00003708112,"domain_scores_codex":[0.9989479,0.0001125704,0.0003372047,0.0003254215,0.00007470549,0.0002022173],"domain_scores_gemma":[0.9993411,0.00007195137,0.0001947652,0.0001999031,0.0001652343,0.00002700134],"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":[0.00005080756,0.00004357532,0.01028532,0.00002735422,0.0003129403,2.092596e-7,0.00002897843,0.2671242,0.7149132,0.007096838,0.00000402843,0.0001124981],"study_design_scores_gemma":[0.0005824862,0.0001948527,0.0005727779,0.00001107057,0.0001241073,0.000003131056,0.00005216158,0.9467984,0.04068791,0.0106904,0.000142486,0.0001402635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5330811,0.000280322,0.4662576,0.00005489758,0.00002543111,0.0002477212,0.00002958995,0.000002186908,0.00002109214],"genre_scores_gemma":[0.9762139,0.000005250919,0.02340576,0.00009896467,0.00005152066,0.00006843641,0.0001197837,0.00001317778,0.00002320785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6796741,"threshold_uncertainty_score":0.4561751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859889","doi":"10.5376/cmb.2024.14.0021","title":"Molecular Interactions in Biological Systems: Technological Applications and Innovations","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Various Chemistry Research Topics","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computational biology; Computer science; Nanotechnology; Biology; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.0276157127509553,"gpt":0.3455894996453538,"spread":0.3179737868943985,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001251233,0.0001435426,0.0001593171,0.0001945619,0.00006192213,0.00007180754,0.0001916491,0.0001840573,0.00007877333],"category_scores_gemma":[0.0001625231,0.0001334068,0.00004348711,0.0004938386,0.0003130883,0.00003484738,0.0001646424,0.0004583871,0.00002984982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009746919,"about_ca_system_score_gemma":0.00008961843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001101816,"about_ca_topic_score_gemma":0.000001333572,"domain_scores_codex":[0.9988773,0.00004479693,0.0002959075,0.0004542464,0.00009603283,0.0002316408],"domain_scores_gemma":[0.9993861,0.0002504743,0.00003323722,0.0001779545,0.00009332629,0.00005893271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003692601,0.00005991382,0.001285894,0.00007982851,0.00004644484,0.00008884269,0.00001066358,0.002056604,0.324785,0.6670487,0.00002957568,0.004504819],"study_design_scores_gemma":[0.0006350901,0.00005908668,0.0006095317,0.0001951388,0.00002731049,0.0008222563,0.000203072,0.1019495,0.01750892,0.8215375,0.05574492,0.0007076924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4286719,0.00327496,0.5587325,0.002097916,0.00005677496,0.0002524222,0.00009429608,0.0003601108,0.006459171],"genre_scores_gemma":[0.9962117,0.00001899849,0.00281383,0.00007071313,0.000037474,0.0003712137,0.0003950516,0.00001350614,0.00006750115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5675398,"threshold_uncertainty_score":0.544017,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4214612010","doi":"10.5376/cmb.2022.12.0001","title":"Genomewide Identification and Expression Analyses of <i>PAL</i> Genes in Different Color Radish","year":2022,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Plant Gene Expression Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Anthocyanin; Biology; Raphanus; Botany","retraction":null,"screen_n_in":null,"score":{"opus":0.01448351584211953,"gpt":0.2927928706720047,"spread":0.2783093548298851,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002285132,0.0001894143,0.0003042584,0.0002799152,0.0001217787,0.00001326102,0.0002752749,0.0001097987,0.00004732155],"category_scores_gemma":[0.00007692336,0.000182026,0.0001229644,0.0002320228,0.0001267404,0.000004331337,0.0004210101,0.0000852349,0.000001798163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003346309,"about_ca_system_score_gemma":0.00006694659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005041377,"about_ca_topic_score_gemma":0.00000947119,"domain_scores_codex":[0.9981086,0.0004570807,0.0004761493,0.0005541343,0.0002005904,0.0002034745],"domain_scores_gemma":[0.9992268,0.00006306481,0.0002689542,0.0002732202,0.00009499735,0.00007293394],"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":[0.00009081014,0.0001194082,0.001954901,0.00001347398,0.00009118023,0.000007633261,0.00002830835,0.03824996,0.9575856,0.0007695775,0.0005141837,0.0005749544],"study_design_scores_gemma":[0.001383436,0.0003612354,0.02594719,0.0000193072,0.00009402531,0.00005350403,0.00005594772,0.01610149,0.9414011,0.006955591,0.007160415,0.0004667206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9408442,0.00328783,0.05521068,0.0001323192,0.00009247005,0.0001883339,0.0001504645,0.000009251878,0.00008445066],"genre_scores_gemma":[0.9951085,0.000110192,0.001718805,0.0001535815,0.00003747179,0.0000865457,0.002666792,0.000017202,0.0001009049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05426431,"threshold_uncertainty_score":0.7422803,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1839112781","doi":"10.5376/cmb.2014.04.0004","title":"In Silico Proteomic Functional Re-annotation of <i>Escherichia coli</i> K-12 using Dynamic Biological Data Fusion Strategy","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Annotation; Proteome; Computational biology; In silico; Data integration; Genome project; Genome; Computer science; Biological database; Human proteome project; Biology; Proteomics; Bioinformatics; Gene; Genetics; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.03123532041575599,"gpt":0.2842681028811567,"spread":0.2530327824654007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003826234,0.0002415276,0.000310317,0.0001103651,0.00009001687,0.00001264264,0.0003503731,0.0002790398,0.00001978372],"category_scores_gemma":[0.0001782495,0.0002348725,0.00008365904,0.0001531956,0.0002505316,0.00000369365,0.0004122366,0.0001036373,0.00000538371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003059533,"about_ca_system_score_gemma":0.0001651639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001205259,"about_ca_topic_score_gemma":0.0000466313,"domain_scores_codex":[0.9980868,0.0003150626,0.0004911128,0.0006897601,0.0001285777,0.0002887633],"domain_scores_gemma":[0.9990507,0.000063622,0.0002371978,0.0004108687,0.000176552,0.00006103788],"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":[0.0001249763,0.00009861312,0.001444648,0.00001433376,0.00006691306,0.000001782791,0.0000144272,0.04705277,0.9456648,0.004708912,0.00007401783,0.0007338335],"study_design_scores_gemma":[0.005930231,0.003405185,0.3391432,0.00007563744,0.0001424536,0.00008266474,0.00006862784,0.4350665,0.08581245,0.1201563,0.008302646,0.001814189],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628036,0.0007493886,0.1355476,0.0001209857,0.0001860067,0.0003186178,0.0001225545,0.000005110134,0.0001460299],"genre_scores_gemma":[0.9847918,0.00004933413,0.01272319,0.000261304,0.0001071241,0.00002535952,0.002003453,0.00002208365,0.0000164017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8598523,"threshold_uncertainty_score":0.9577824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859937","doi":"10.5376/cmb.2024.14.0015","title":"Advances in Biomechanics: Exploring Biophysical Models in Cellular Mechanics","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Biomechanics; Computer science; Medicine; Anatomy","retraction":null,"screen_n_in":null,"score":{"opus":0.02088558662575223,"gpt":0.2873012234551169,"spread":0.2664156368293647,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001646332,0.0001942198,0.000187113,0.0003228837,0.00003056816,0.00003200895,0.0001871168,0.0001567006,0.00001818339],"category_scores_gemma":[0.00002800044,0.0002066196,0.0001214778,0.000377999,0.00002569347,0.00002551518,0.0001401049,0.0001957192,0.0000268139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005026248,"about_ca_system_score_gemma":0.00009054576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001550994,"about_ca_topic_score_gemma":0.00001984053,"domain_scores_codex":[0.9986095,0.00009338633,0.0003164161,0.000582319,0.0001018695,0.0002965356],"domain_scores_gemma":[0.9996356,0.00003099622,0.00003579207,0.0001880303,0.00005130903,0.00005823305],"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":[0.00001710702,0.00005994819,0.000003260363,0.00001908391,0.00001455002,0.00004426453,0.00002520584,0.0301451,0.6690303,0.2959239,0.00001132641,0.004705957],"study_design_scores_gemma":[0.0002569115,0.0001818685,0.000002376732,0.00005368374,0.000006307324,0.00001703511,0.00004122917,0.4865741,0.08041513,0.4280063,0.004202161,0.0002428469],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1490214,0.006023233,0.8435726,0.0001548456,0.0006653062,0.0001731218,0.00001783197,0.00002754591,0.0003441883],"genre_scores_gemma":[0.9966325,0.0005707028,0.00195487,0.0001349239,0.0001030561,0.00009631803,0.0004550232,0.00003432651,0.00001834917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.847611,"threshold_uncertainty_score":0.8425702,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4395683067","doi":"10.5376/cmb.2024.14.0002","title":"Artificial Intelligence and Drug Design: Future Prospects and Ethical Considerations","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Drug; Engineering ethics; Management science; Computer science; Psychology; Medicine; Engineering; Pharmacology","retraction":null,"screen_n_in":null,"score":{"opus":0.04085469110490558,"gpt":0.3539375980826684,"spread":0.3130829069777629,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005478889,0.0001522866,0.0001514303,0.0001663008,0.0001504585,0.0003265078,0.0001510223,0.0001306039,0.000009123119],"category_scores_gemma":[0.0001943206,0.000144739,0.00003820399,0.0002834761,0.0002318471,0.0001126,0.0002423897,0.0003386158,0.00001581562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002422103,"about_ca_system_score_gemma":0.000246619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002975841,"about_ca_topic_score_gemma":0.000001570673,"domain_scores_codex":[0.9983755,0.000457618,0.0002614016,0.0005761718,0.0001536155,0.000175687],"domain_scores_gemma":[0.9979281,0.001700362,0.00003413669,0.0001313433,0.0001131032,0.00009298108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003108372,0.00001059754,0.000008694866,0.00001484727,0.00002327113,0.00005641244,0.000322261,0.04459543,0.000564657,0.9258019,0.0001086761,0.02849016],"study_design_scores_gemma":[0.00001680245,0.0000346025,0.0001658146,0.000009502794,0.000004494835,0.0001474358,0.000007978328,0.4110625,0.0003664659,0.5880131,0.00007648912,0.00009474943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006254491,0.002216589,0.9674541,0.02319451,0.0004612828,0.0002111298,0.000006645867,0.0001377644,0.0000634348],"genre_scores_gemma":[0.6139311,0.000008046974,0.3849523,0.0009919679,0.00008285991,0.00001472229,0.000009947519,0.000006303304,0.000002738104],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6076766,"threshold_uncertainty_score":0.5902284,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2239593216","doi":"10.5376/cmb.2015.05.0006","title":"Comparative study of Codon usage pattern and compositional distribution between whole genome and virulence gene set of <i>Vibrio cholerae N16961</i>","year":2015,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Vibrio bacteria research studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Vibrio cholerae; Virulence; Biology; Gene; Genome; Genetics; Microbiology; Bacteria","retraction":null,"screen_n_in":null,"score":{"opus":0.03149290872155126,"gpt":0.3193551107635436,"spread":0.2878622020419924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002760529,0.0002098259,0.0004295043,0.00007405966,0.0000853305,0.00001657302,0.0001504802,0.0001178881,0.000002673332],"category_scores_gemma":[0.00006071671,0.0002089907,0.00004778152,0.0001103277,0.0005190761,0.000009720946,0.0004296311,0.00008453215,0.000002249367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002712526,"about_ca_system_score_gemma":0.00009462653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001707325,"about_ca_topic_score_gemma":0.00001540152,"domain_scores_codex":[0.9983261,0.0003679321,0.0003935889,0.0004576284,0.0002300665,0.0002247269],"domain_scores_gemma":[0.998947,0.00007437206,0.0002118142,0.0001835357,0.0004492791,0.0001340467],"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":[0.0001233219,0.0001500795,0.01344979,0.00002574469,0.000313476,0.000005903561,0.000289155,0.001045054,0.9839827,0.0002101232,0.00008876443,0.0003158305],"study_design_scores_gemma":[0.005767289,0.006059963,0.8870926,0.00004313093,0.0001361768,0.00006737631,0.0002416914,0.001432036,0.0936745,0.002768355,0.002086915,0.0006299828],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9256268,0.001219448,0.07059683,0.0001189811,0.00003205836,0.0004187332,0.001958249,0.000006233033,0.00002270652],"genre_scores_gemma":[0.9937165,0.00002801945,0.000829691,0.00003706366,0.00007297166,0.000027799,0.00526956,0.00001183292,0.000006589201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8903083,"threshold_uncertainty_score":0.8522394,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124693293","doi":"10.5376/cmb.2014.04.0002","title":"GC2 Biology Dictates Gene Expressivity in <i>Camellia sinensis</i>","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Codon usage bias; Gene; Biology; Genetics; Synonymous substitution; Gene expression; GC-content; Natural selection; Selection (genetic algorithm); Genome","retraction":null,"screen_n_in":null,"score":{"opus":0.007743715591556165,"gpt":0.2476628474223675,"spread":0.2399191318308114,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004591405,0.000354525,0.0004114569,0.0001684651,0.0001079794,0.00002398635,0.0004196228,0.0004789321,0.00004710287],"category_scores_gemma":[0.0003347919,0.000348207,0.0001745623,0.0001734191,0.0002792769,0.000005742381,0.0002443085,0.0001255928,0.0000658563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002390354,"about_ca_system_score_gemma":0.00009263471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008298701,"about_ca_topic_score_gemma":0.00001574302,"domain_scores_codex":[0.9974554,0.0005836221,0.0004660542,0.0008430165,0.0001268582,0.0005250754],"domain_scores_gemma":[0.9989193,0.0001289509,0.0001932167,0.0004538323,0.0001662477,0.0001384375],"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":[0.00007446183,0.00009032751,0.0009310453,0.00001048594,0.00006180744,0.000008797688,0.0000160311,0.00259217,0.9531616,0.03756977,0.0001952991,0.005288213],"study_design_scores_gemma":[0.001516897,0.0008724442,0.004546867,0.0000308921,0.00003237912,0.0000632482,0.000004636805,0.003285294,0.7259949,0.2273599,0.03548214,0.0008103771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5894644,0.001778543,0.4071295,0.0003460157,0.0002645717,0.0002480983,0.00003866721,0.00003059206,0.0006996842],"genre_scores_gemma":[0.9758253,0.00006899287,0.02177461,0.0009049097,0.0002259495,0.00008398278,0.0009896889,0.00004534576,0.00008117926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.386361,"threshold_uncertainty_score":0.999897,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859867","doi":"10.5376/cmb.2024.14.0006","title":"Modeling Biological Networks: A Systematic Review of Computational Approaches to Network Dynamics","year":2024,"lang":"en","type":"review","venue":"Computational Molecular Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science; Dynamics (music); Biological network; Network dynamics; Management science; Data science; Computational biology; Biology; Psychology; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0670123840900099,"gpt":0.315993772986306,"spread":0.2489813888962961,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008902915,0.0007767984,0.003071721,0.000254884,0.00008108195,0.00003203403,0.0007726714,0.0007374474,0.000008206006],"category_scores_gemma":[0.0002048595,0.000619597,0.001508038,0.0009719334,0.0001498981,0.000002511625,0.0006510539,0.0003323327,0.00004241901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001127809,"about_ca_system_score_gemma":0.000412412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002131295,"about_ca_topic_score_gemma":0.000002892444,"domain_scores_codex":[0.9951069,0.001034125,0.001849596,0.001202133,0.000278692,0.0005285182],"domain_scores_gemma":[0.9982033,0.0001263005,0.000545113,0.0006667597,0.0002638265,0.0001946756],"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":[0.000005039321,0.00003395999,7.882725e-7,0.2586094,0.001538502,0.00000731432,0.000001978372,0.7251853,5.697166e-7,0.01052352,0.0002967682,0.00379694],"study_design_scores_gemma":[0.0001416558,0.0003265574,2.231872e-7,0.297385,0.006113626,0.0002278213,0.000006181158,0.6705831,1.892694e-7,0.01852794,0.005511672,0.001176061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000005217298,0.5596482,0.4389712,0.00003720642,0.0001346212,0.001065244,0.00008996781,0.00002145871,0.00002691894],"genre_scores_gemma":[0.001544962,0.9676604,0.01445512,0.0005259242,0.0003681329,0.0004687735,0.01482636,0.0001198988,0.00003040954],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.4245161,"threshold_uncertainty_score":0.9996256,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405860005","doi":"10.5376/cmb.2024.14.0010","title":"Advances in Causal Inference Methods for Biological Network Analysis","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Causal inference; Inference; Computer science; Computational biology; Artificial intelligence; Biology; Econometrics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03595013738816205,"gpt":0.4569234671809553,"spread":0.4209733297927933,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194678,0.0001943864,0.0003571368,0.0004818173,0.00006556182,0.0001206978,0.0005956169,0.0001228242,0.00001115509],"category_scores_gemma":[0.0004076735,0.0001738811,0.000238687,0.002311711,0.0001099824,0.0002414837,0.0002725965,0.0001635164,0.0000102673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006735027,"about_ca_system_score_gemma":0.0002065884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005418344,"about_ca_topic_score_gemma":0.000005365173,"domain_scores_codex":[0.9974952,0.0008408986,0.0004142127,0.0007735767,0.0001091879,0.0003669747],"domain_scores_gemma":[0.9946947,0.004821663,0.00006702005,0.0002166432,0.0001297487,0.00007023145],"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":[0.000004656781,0.00001242643,0.000391287,0.000006675457,0.00006024224,0.000006917038,0.00002161703,0.4435731,0.0001033121,0.4701047,0.000007616637,0.08570744],"study_design_scores_gemma":[0.00007398199,0.00005455867,0.001734279,0.000007213252,0.00001506562,0.000004242845,0.000001107607,0.499817,0.00002084813,0.4966247,0.001536264,0.0001106929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001979507,0.006724897,0.9893717,0.0006595075,0.000597123,0.0002433989,0.00001306293,0.0001466087,0.0002641862],"genre_scores_gemma":[0.3886605,0.00002519875,0.6107789,0.0002859381,0.00005481661,0.00007285086,0.0001127195,0.000005976753,0.000003099873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.386681,"threshold_uncertainty_score":0.7090666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405860008","doi":"10.5376/cmb.2024.14.0022","title":"Protein-Protein Interaction Networks in Rice under Drought Stress: Insights from Proteomics and Bioinformatics Analysis","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Proteomics; Drought stress; Computational biology; Bioinformatics; Protein Interaction Networks; Biology; Computer science; Protein–protein interaction; Genetics; Botany; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.007986623571956113,"gpt":0.2825331537579408,"spread":0.2745465301859847,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003831133,0.0001608261,0.0001916091,0.0001895372,0.00005608287,0.00006557321,0.0001218142,0.0001834163,0.00002706667],"category_scores_gemma":[0.00001558896,0.0001567523,0.00007022914,0.0004632836,0.00007512349,0.0001003475,0.00009683197,0.0002994263,0.000005656552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000086847,"about_ca_system_score_gemma":0.00003847345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000690226,"about_ca_topic_score_gemma":0.00001725164,"domain_scores_codex":[0.9990778,0.00002858137,0.0003414763,0.000335923,0.00007132701,0.0001449179],"domain_scores_gemma":[0.9995521,0.00007912741,0.0001007058,0.0001705883,0.00005173413,0.00004578813],"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":[0.00004840472,0.0001088876,0.000325841,0.000160189,0.0005778285,0.00002310097,0.0002063544,0.2636009,0.4386153,0.291689,0.000007609441,0.004636533],"study_design_scores_gemma":[0.0001363684,0.00001583707,0.00009155022,0.00009192853,0.00004966194,0.000002875953,0.00004683423,0.6231946,0.02291832,0.3531619,0.0001115567,0.0001786097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3548188,0.000437378,0.6440599,0.0001458524,0.00001153981,0.0002274236,0.00004432325,0.00007619103,0.0001785467],"genre_scores_gemma":[0.7843425,0.0000120353,0.2143919,0.00006472624,0.00003563769,0.0002623247,0.0008592485,0.00001406961,0.00001755589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.429668,"threshold_uncertainty_score":0.6392174,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4395674767","doi":"10.5376/cmb.2024.14.0003","title":"The Application of Artificial Intelligence in Drug Discovery: Opportunities and Challenges","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Drug discovery; Data science; Computer science; Artificial intelligence; Bioinformatics; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.06149179061231489,"gpt":0.3470604638194853,"spread":0.2855686732071704,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006035136,0.00009610422,0.0001176689,0.000156324,0.00005276755,0.0001049059,0.0003572943,0.00003515554,4.093005e-7],"category_scores_gemma":[0.00006448278,0.00007686614,0.00003938385,0.0002427234,0.0002160406,0.0001867097,0.0002204117,0.00008753521,0.000002873295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002321912,"about_ca_system_score_gemma":0.0001392533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001946889,"about_ca_topic_score_gemma":0.0000173625,"domain_scores_codex":[0.9988628,0.0002619827,0.0002932088,0.0003201974,0.0001317835,0.0001300232],"domain_scores_gemma":[0.9983633,0.0013254,0.00005562216,0.0001638723,0.00006512685,0.00002674585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002900281,0.00001057635,0.000004901933,0.00001784723,0.000009206458,0.000004520099,0.0001645884,0.02740214,0.0001464816,0.6679611,0.00000206452,0.3042737],"study_design_scores_gemma":[0.00001101664,0.00001579547,0.0003212526,0.00001048624,0.000001407785,0.000008224074,0.00005429893,0.4396049,0.0001224468,0.5593315,0.0004713841,0.00004732233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009034709,0.01039328,0.9722405,0.007780921,0.000186649,0.0001309508,0.000005819139,0.00003397052,0.0001932078],"genre_scores_gemma":[0.954037,0.0004391786,0.04536886,0.00007296252,0.00002400837,0.00003147545,0.00001468468,0.000005962356,0.000005902894],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9450023,"threshold_uncertainty_score":0.313451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318591807","doi":"10.5376/cmb.2023.13.0001","title":"Comparative Analysis of Alternative Splicing Events in Foliar Transcriptomes of Potato Plants Inoculated with <i>Phytophthora Infestans</i>","year":2023,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Plant-Microbe Interactions and Immunity","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Youngstown State University","keywords":"Phytophthora infestans; Biology; Gene; Exon; Intron; Genetics; Alternative splicing; Inoculation; Transcriptome; Gene expression; Horticulture","retraction":null,"screen_n_in":null,"score":{"opus":0.02244129262912976,"gpt":0.2865986250893483,"spread":0.2641573324602186,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001408399,0.0001409483,0.0004454578,0.0002221676,0.00005492439,0.000004955945,0.0001907413,0.00007311582,0.00003723065],"category_scores_gemma":[0.00001723297,0.00007067693,0.0001193475,0.00144985,0.0001017693,0.00005572384,0.00004209876,0.00009690004,0.0000053916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002531096,"about_ca_system_score_gemma":0.00001842485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002648363,"about_ca_topic_score_gemma":0.0006991602,"domain_scores_codex":[0.9988757,0.0002057728,0.0003650403,0.000233055,0.0001318313,0.0001886278],"domain_scores_gemma":[0.9992086,0.0003271605,0.0002079783,0.00005128151,0.0001672537,0.00003772611],"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":[0.0001332513,0.0001313594,0.01580887,0.000005682516,0.000608588,0.00001313488,0.0002739984,0.04038497,0.9389229,0.003164214,0.000008285714,0.0005447399],"study_design_scores_gemma":[0.0003911854,0.0003770089,0.9574721,0.00006648146,0.00009599782,0.00001154266,0.0001598372,0.01668711,0.01910474,0.005354928,0.00009769986,0.0001814055],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971213,0.00008293929,0.001881115,0.00008258181,0.00004527206,0.000157439,0.0004520822,0.00001831805,0.0001589029],"genre_scores_gemma":[0.9982172,0.00001565468,0.0002619043,0.00003761702,0.000006904284,0.000009604824,0.00143857,0.000001070429,0.00001145084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9416632,"threshold_uncertainty_score":0.2882122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387029309","doi":"10.5376/cmb.2023.13.0003","title":"Computational Molecular Biology Interdisciplinary Technological Integration and New Advances","year":2023,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Scope (computer science); Systems biology; Computational biology; Computer science; Biology; Computational model; Data science; Management science; Nanotechnology; Artificial intelligence; Engineering; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.01887304333335217,"gpt":0.3501770350842353,"spread":0.3313039917508832,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003403375,0.0002656567,0.000260536,0.0002979429,0.0001497139,0.00003953484,0.0003561347,0.0004209871,0.00002357048],"category_scores_gemma":[0.0004092238,0.0002327317,0.0001155182,0.0003755621,0.0008339662,0.000009617439,0.0008638336,0.0002257586,0.00008087439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002270003,"about_ca_system_score_gemma":0.0001805456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004650694,"about_ca_topic_score_gemma":0.000004071584,"domain_scores_codex":[0.9981463,0.0001574153,0.0004424298,0.0005857268,0.000210483,0.0004576213],"domain_scores_gemma":[0.9991271,0.0001072455,0.0001165667,0.0002261015,0.0001998619,0.0002230489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002182451,0.0001524822,0.001864631,0.00007770394,0.0002539459,0.00005225173,0.0001405811,0.01386423,0.4960148,0.1577859,0.003697182,0.3258781],"study_design_scores_gemma":[0.001134513,0.001217026,0.002411535,0.00002923729,0.00002068477,0.00008924046,0.0001912738,0.02571712,0.01141041,0.950081,0.007207517,0.0004904316],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1793327,0.001233727,0.8154773,0.002788079,0.0001794307,0.0002777157,0.00005687511,0.00008720037,0.0005670098],"genre_scores_gemma":[0.9617092,0.0001657939,0.03395637,0.0006897337,0.0001126088,0.00003311488,0.00319635,0.0000231411,0.0001137068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7922952,"threshold_uncertainty_score":0.9490525,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417017350","doi":"10.5376/cmb.2025.15.0014","title":"Pretrained Language Models for Biological Sequence Understanding","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Interpretability; Sequence (biology); Field (mathematics); Biological data; Biological database; Function (biology); Computational model; Language model","retraction":null,"screen_n_in":null,"score":{"opus":0.04331735219292956,"gpt":0.3521607780462377,"spread":0.3088434258533082,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004968931,0.0004543631,0.0004455013,0.0002217389,0.000289875,0.00007575684,0.0005725512,0.0007788863,0.00002476926],"category_scores_gemma":[0.0006694919,0.000458451,0.0003308801,0.0003037091,0.0005469004,0.000009762827,0.0003766873,0.0003054581,0.000007722557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710103,"about_ca_system_score_gemma":0.000522715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006545049,"about_ca_topic_score_gemma":0.000002746082,"domain_scores_codex":[0.9974326,0.000303853,0.00071071,0.0007926825,0.0001320385,0.0006281448],"domain_scores_gemma":[0.9986352,0.0002822455,0.00028061,0.0004287137,0.0002524788,0.0001207128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002763551,0.0001032454,0.0004275365,0.000212773,0.0004794007,0.000006393257,0.0001916141,0.1256361,0.08696824,0.781348,0.000642385,0.003707958],"study_design_scores_gemma":[0.001598834,0.0006945307,0.00004339074,0.00007985068,0.00007230268,0.00002382888,0.0002758597,0.4751679,0.001877785,0.5173649,0.002282186,0.0005185961],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01689737,0.002339864,0.9708333,0.001332203,0.0005439686,0.0009308524,0.0004011542,0.00004651136,0.006674775],"genre_scores_gemma":[0.9249498,0.00005616987,0.06863124,0.002394588,0.00011538,0.00008083262,0.003507009,0.00003075994,0.0002342041],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9080524,"threshold_uncertainty_score":0.9997867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859873","doi":"10.5376/cmb.2024.14.0009","title":"The Application and Progress of Deep Learning in Bioinformatics","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Ideological and Political Education","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Hainan University","keywords":"Computer science; Artificial intelligence; Bioinformatics; Computational biology; Data science; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01005711527711655,"gpt":0.3546300442532558,"spread":0.3445729289761393,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002491579,0.00002469904,0.00003837855,0.00002831882,0.00008578604,0.00001840765,0.00004737542,0.00004086981,0.000003551418],"category_scores_gemma":[0.0001516742,0.00001707008,0.00001188151,0.0001262345,0.0003838708,0.00001885656,0.00001536419,0.00005835589,0.000005450219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001275458,"about_ca_system_score_gemma":0.00004995957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004739556,"about_ca_topic_score_gemma":0.00002170516,"domain_scores_codex":[0.9996106,0.00009797974,0.0001048625,0.00005483444,0.00004700714,0.00008471524],"domain_scores_gemma":[0.9995629,0.0003407418,0.00001939255,0.00001919105,0.00003796968,0.0000197668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001118827,0.000004496034,0.001626491,0.000007269156,0.000002372163,1.867786e-7,0.0007397051,0.0002377802,0.00001915478,0.8802346,0.000002197397,0.1171246],"study_design_scores_gemma":[0.00003503599,0.0000309677,0.008606824,0.000006786662,0.000002533189,9.504722e-7,0.0004214415,0.05982133,0.00001059219,0.9219874,0.009039981,0.00003611594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3684546,0.005658534,0.6016837,0.01579407,0.0002465868,0.0003695481,0.000002122629,0.00006881641,0.007722035],"genre_scores_gemma":[0.9977607,0.00001971732,0.002083348,0.00007131087,0.00002121217,0.00001376463,0.00001245687,0.000001211979,0.00001633724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.629306,"threshold_uncertainty_score":0.1414388,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1905128537","doi":"10.5376/cmb.2013.03.0004","title":"Predicting Long Non-coding RNAs Based on Genomic Sequence Information","year":2013,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computational biology; Coding (social sciences); Sequence (biology); Biology; Genetics; Computer science; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01027559484360192,"gpt":0.280335000804534,"spread":0.2700594059609321,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001914455,0.0002181975,0.0001572291,0.0002015369,0.0001383519,0.00007144865,0.000307051,0.0002537066,0.00007700186],"category_scores_gemma":[0.0001822051,0.000230791,0.0001150822,0.0001778001,0.0001064373,0.00001589246,0.0001543667,0.0002217304,0.0002196806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008687597,"about_ca_system_score_gemma":0.0003180616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005685562,"about_ca_topic_score_gemma":0.000003700024,"domain_scores_codex":[0.9985223,0.0001289634,0.0003238201,0.0003993646,0.0002277014,0.0003978608],"domain_scores_gemma":[0.9990665,0.00004097117,0.0001359386,0.0003160844,0.0003085058,0.000132052],"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":[0.00004976237,0.00003067687,0.0003323586,0.000021276,0.00005513184,0.000006571468,0.00001609947,0.1938943,0.797291,0.002353949,0.0001858826,0.005762989],"study_design_scores_gemma":[0.002339966,0.001360967,0.007048793,0.00007927891,0.00002841225,0.00007714438,0.00003759908,0.6433888,0.3298946,0.01364587,0.001304142,0.0007944684],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1380809,0.00005632485,0.8596812,0.0003729979,0.0001711174,0.0004997024,0.00002436711,0.00002962468,0.00108378],"genre_scores_gemma":[0.9881586,0.000006353652,0.008209618,0.002089757,0.0001036912,0.0001310938,0.001245095,0.00002898824,0.00002682903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8514716,"threshold_uncertainty_score":0.9411383,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406456968","doi":"10.5376/cmb.2024.14.0019","title":"Biostatistical Challenges in High-Dimensional Data Analysis: Strategies and Innovations","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Data science; Computer science; Management science; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04873716827079277,"gpt":0.3580495397009335,"spread":0.3093123714301407,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003713242,0.0001365114,0.0001720026,0.0003485411,0.00004070712,0.00007730188,0.0002500728,0.0001685343,0.00001631362],"category_scores_gemma":[0.0001942562,0.000121245,0.00003524771,0.0004071087,0.0003248482,0.00001113706,0.0003958627,0.0001371084,0.00001296482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000994386,"about_ca_system_score_gemma":0.0003212307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002738284,"about_ca_topic_score_gemma":0.00005206656,"domain_scores_codex":[0.9986829,0.00009457382,0.000310522,0.0005098378,0.0001652935,0.0002368415],"domain_scores_gemma":[0.9993907,0.00009653706,0.00002896797,0.0002899219,0.0001093182,0.00008452132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009084665,0.0002196711,0.001397501,0.0002560278,0.001601354,0.0001130602,0.0001330698,0.008084767,0.04157894,0.8386503,0.001627339,0.1062471],"study_design_scores_gemma":[0.001386876,0.001058303,0.06017251,0.00008131436,0.0002823667,0.0001072288,0.0005024233,0.3716195,0.001059989,0.5492929,0.01351087,0.000925822],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2528076,0.01495554,0.7257954,0.004959577,0.0002418674,0.0002423007,0.0005161903,0.00003283023,0.0004486711],"genre_scores_gemma":[0.9729581,0.000356219,0.02075005,0.0002422279,0.00007280645,0.0000102659,0.005585006,0.000009849323,0.0000154814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7201505,"threshold_uncertainty_score":0.4944229,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406457134","doi":"10.5376/cmb.2024.14.0016","title":"Dynamic Modeling in Systems Biology: From Pathway Analysis to Whole-Cell Simulations","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Systems biology; Computational biology; Computer science; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01047165479079525,"gpt":0.2740645748759938,"spread":0.2635929200851985,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001801805,0.0002502845,0.0003508984,0.0006203744,0.00006172588,0.0000559043,0.0002621167,0.000282852,0.00001704605],"category_scores_gemma":[0.00003662337,0.0002592869,0.0002659714,0.001126181,0.00005232983,0.000003516644,0.0001604865,0.0001279589,0.00005102468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006544511,"about_ca_system_score_gemma":0.0001416731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001170516,"about_ca_topic_score_gemma":0.0001408783,"domain_scores_codex":[0.997978,0.0002558099,0.0004583338,0.0008733317,0.0001056526,0.0003288702],"domain_scores_gemma":[0.9992574,0.00006773452,0.00005591711,0.0003751378,0.0001267249,0.000117092],"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":[0.000009544656,0.00002277923,0.001732795,0.000005843242,0.000541764,0.000008043203,0.0000224002,0.7675566,0.2284772,0.001146899,0.00001852454,0.0004575811],"study_design_scores_gemma":[0.0002007896,0.00007446583,0.0004531833,0.0000124145,0.000204242,0.000002067459,0.00002143423,0.9887172,0.0006002315,0.008438938,0.0009875268,0.0002875368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4722417,0.004229258,0.5229198,0.0001185802,0.0001264861,0.0001225795,0.000188596,0.00002178523,0.000031171],"genre_scores_gemma":[0.9866996,0.000008610883,0.006077568,0.0001939413,0.00008570946,0.00003736434,0.006780678,0.00003398641,0.00008248392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5168422,"threshold_uncertainty_score":0.9999859,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4378085747","doi":"10.5376/cmb.2022.12.0005","title":"Identification and Expression Characteristic Analysis of CML Gene Family of Melon","year":2022,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Advances in Cucurbitaceae Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Melon; Gene family; Biology; Gene; Fusarium oxysporum; Jasmonic acid; Genetics; Gene expression; Horticulture","retraction":null,"screen_n_in":null,"score":{"opus":0.01171926629498912,"gpt":0.3302516399535025,"spread":0.3185323736585134,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002242501,0.00007914827,0.0001887467,0.0001999541,0.00006318634,0.000003090066,0.0001704046,0.00005121878,0.00001757277],"category_scores_gemma":[0.00008598941,0.00008534355,0.0000798542,0.000312554,0.0001559303,0.000002279849,0.0003057101,0.00006331106,3.84132e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001254495,"about_ca_system_score_gemma":0.0000513562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004186891,"about_ca_topic_score_gemma":4.330524e-7,"domain_scores_codex":[0.9989561,0.0002039447,0.0002853087,0.0002893739,0.0001578971,0.0001073797],"domain_scores_gemma":[0.9993601,0.00004651391,0.0002060911,0.0002021212,0.0001539406,0.0000312636],"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":[0.00006392981,0.00005234779,0.003854238,0.00001298895,0.0002195046,0.000001611609,0.00002204259,0.01986662,0.9735425,0.001527661,0.00001585063,0.0008207724],"study_design_scores_gemma":[0.0007961061,0.0008310085,0.09864124,0.000007541216,0.0002975724,0.00002168009,0.0001257279,0.02251246,0.8585947,0.0171213,0.0007192418,0.0003314329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7286728,0.001045796,0.2699451,0.0000263225,0.00004087712,0.00008450317,0.0001599286,0.000002461101,0.00002222333],"genre_scores_gemma":[0.9947044,0.00003462198,0.003495313,0.00004711113,0.0000126722,0.00002977459,0.001648571,0.000008709303,0.0000188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2664498,"threshold_uncertainty_score":0.3480209,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417022540","doi":"10.5376/cmb.2025.15.0006","title":"Genetic Regulation of Pigmentation in <i>Brassica napus</i> Flowers","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Plant Gene Expression Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Gene; Petal; Epigenetics; Regulator gene; Carotenoid; Brassica; Regulation of gene expression; Mechanism (biology); Orange (colour)","retraction":null,"screen_n_in":null,"score":{"opus":0.004879749670257691,"gpt":0.2624346685808512,"spread":0.2575549189105936,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003808418,0.0004907328,0.000665247,0.0008310881,0.0001251333,0.00003476214,0.0005364874,0.0007087697,0.0001186749],"category_scores_gemma":[0.0003051894,0.0005671963,0.00037752,0.000990233,0.0004286984,0.00001178698,0.0003548518,0.000190737,0.00002345129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374438,"about_ca_system_score_gemma":0.0006964343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009964524,"about_ca_topic_score_gemma":0.00005429909,"domain_scores_codex":[0.9958706,0.0008117395,0.001315956,0.001147778,0.0003330444,0.0005208196],"domain_scores_gemma":[0.9980937,0.0001488391,0.0005745279,0.0005983713,0.0004507288,0.0001338289],"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":[0.0002517386,0.0002634123,0.001907318,0.00007431893,0.0004347196,0.00001185228,0.00004633611,0.1098041,0.8675572,0.0111363,0.002642089,0.005870633],"study_design_scores_gemma":[0.009460656,0.001287642,0.09712767,0.000716333,0.0008868385,0.00006621316,0.0001014692,0.1976223,0.5515306,0.1023718,0.03653762,0.002290924],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5715252,0.004754929,0.4187589,0.0007359388,0.0005680674,0.0005460188,0.0001467814,0.00001579605,0.002948418],"genre_scores_gemma":[0.9828684,0.0003089508,0.01315828,0.0005776255,0.00008341225,0.00005426577,0.002417443,0.00003530925,0.0004962988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4113433,"threshold_uncertainty_score":0.999678,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406457214","doi":"10.5376/cmb.2024.14.0017","title":"The Evolving Landscape of Genomic Selection: Insights and Innovations in Quantitative Genetics","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Quantitative genetics; Evolutionary biology; Biology; Human evolutionary genetics; Computational biology; Genetics; Genome; Computer science; Genetic variation; Artificial intelligence; Gene","retraction":null,"screen_n_in":null,"score":{"opus":0.01202599860366853,"gpt":0.2713814821867188,"spread":0.2593554835830503,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009926099,0.00006720047,0.00007032288,0.00006235632,0.00007428654,0.00002408821,0.00007003548,0.00006214423,0.000004406045],"category_scores_gemma":[0.00003871264,0.00005358616,0.00002437358,0.0001276244,0.00009553645,0.000001581722,0.00006260102,0.00005231582,0.000002343754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004071331,"about_ca_system_score_gemma":0.00006861713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005795287,"about_ca_topic_score_gemma":0.00001321469,"domain_scores_codex":[0.9994919,0.00005206869,0.000150196,0.0001747779,0.00004065844,0.00009045019],"domain_scores_gemma":[0.9997495,0.00006509749,0.00003166117,0.00005075489,0.00008564199,0.00001736087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004160452,0.00003297947,0.017405,0.00004295584,0.0001500437,0.000006696917,0.0002523545,0.01189791,0.7528513,0.2133145,0.0008172503,0.003187397],"study_design_scores_gemma":[0.001974538,0.002369559,0.3060279,0.0001762656,0.00009882578,0.0002169498,0.001253606,0.2271356,0.02406707,0.3801399,0.05547839,0.001061365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425511,0.01389644,0.04270779,0.0002047158,0.00009224511,0.00007249833,0.00001489981,0.0000057435,0.0004545312],"genre_scores_gemma":[0.9960322,0.0002315156,0.003520494,0.00006619276,0.00002574347,0.000003805288,0.00007829138,0.000004576929,0.0000371633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7287843,"threshold_uncertainty_score":0.218518,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7108733851","doi":"10.5376/cmb.2025.15.0010","title":"Computational Prediction of Off-Target Effects in CRISPR Systems CRISPR","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"CRISPR; Process (computing); Computational model; Field (mathematics); Key (lock); Genome editing","retraction":null,"screen_n_in":null,"score":{"opus":0.004256948384104758,"gpt":0.3087525044499577,"spread":0.3044955560658529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000377384,0.0004546037,0.000663102,0.000527234,0.00008644989,0.00003321224,0.00036612,0.000612954,0.00001140973],"category_scores_gemma":[0.0002746016,0.000539032,0.0002533214,0.0005484807,0.0002719102,0.000007677195,0.0002627101,0.0002892622,0.000006801495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009046945,"about_ca_system_score_gemma":0.0005043793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004428546,"about_ca_topic_score_gemma":0.000003515274,"domain_scores_codex":[0.9968765,0.0004253426,0.001086817,0.0008701576,0.0002362611,0.000504927],"domain_scores_gemma":[0.9985681,0.0002357396,0.0002484919,0.0003746821,0.0004606192,0.0001124178],"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":[0.0001358619,0.0002884214,0.01007913,0.0006752679,0.0004353248,0.00001751306,0.0000556375,0.8935465,0.05523413,0.0355102,0.0006510985,0.003370861],"study_design_scores_gemma":[0.00558165,0.001320848,0.04247586,0.0007366611,0.0002310181,0.0000622048,0.0001359787,0.8328239,0.03220125,0.0772306,0.006302143,0.0008978689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08873152,0.03149154,0.8760366,0.0002320981,0.001636045,0.0007885076,0.0002344013,0.00001992198,0.0008293095],"genre_scores_gemma":[0.9877115,0.0001308197,0.01001278,0.0002623083,0.0001293247,0.00008338531,0.001553313,0.00003741246,0.00007917731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.89898,"threshold_uncertainty_score":0.9997061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7108681698","doi":"10.5376/cmb.2025.15.0015","title":"High-Performance Computing Pipelines for NGS Variant Calling","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Workflow; Orchestration; Benchmark (surveying); Process (computing); Task (project management); Mutation; Pipeline transport; SPARK (programming language); Middleware (distributed applications)","retraction":null,"screen_n_in":null,"score":{"opus":0.008896724754100378,"gpt":0.2745310821449927,"spread":0.2656343573908923,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003817487,0.0004906163,0.0005586823,0.0001673173,0.0004645025,0.0000578223,0.0004691851,0.0004492101,0.000005561978],"category_scores_gemma":[0.0001961737,0.0005316774,0.0002623342,0.0002455778,0.0003218592,0.000001403414,0.0004831303,0.0001703033,0.000007551815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004799708,"about_ca_system_score_gemma":0.0004670566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000229512,"about_ca_topic_score_gemma":0.000004217183,"domain_scores_codex":[0.9972581,0.0001469578,0.000806963,0.001030293,0.0001047233,0.0006530135],"domain_scores_gemma":[0.9983705,0.0002011355,0.0002751638,0.0003765036,0.0006819231,0.00009482195],"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":[0.000428966,0.0002535649,0.00300157,0.0004073726,0.001619778,0.000009305753,0.00008063352,0.3816887,0.4284032,0.1376457,0.000825776,0.0456354],"study_design_scores_gemma":[0.01125341,0.003357416,0.01272952,0.0005332936,0.001016333,0.00011381,0.0001878113,0.4377851,0.141278,0.3242489,0.06416737,0.003329094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4271927,0.006235528,0.5636631,0.0008130698,0.001245593,0.0004863587,0.0001217274,0.000007030079,0.0002349696],"genre_scores_gemma":[0.9375694,0.0003064166,0.0588016,0.001922136,0.0004763336,0.00005291438,0.0006525893,0.00004147035,0.0001770964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5103768,"threshold_uncertainty_score":0.9997135,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7108687077","doi":"10.5376/cmb.2025.15.0019","title":"Mathematical Modeling of Synthetic Genetic Circuits","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Synthetic biology; Electronic circuit; Genetic programming; Genetic algorithm; Mathematical model; Stability (learning theory); Experimental data; Synthetic data; Ordinary differential equation","retraction":null,"screen_n_in":null,"score":{"opus":0.00918685579768262,"gpt":0.2696066488064389,"spread":0.2604197930087562,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003195674,0.0004694313,0.0007506185,0.0003571044,0.0001270733,0.0000261432,0.0006110416,0.0006264138,0.0001727655],"category_scores_gemma":[0.0002281217,0.0005267719,0.000580697,0.0005744699,0.0004950914,0.000002954877,0.0004398548,0.000194883,0.00005786365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005410791,"about_ca_system_score_gemma":0.0006338989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003947355,"about_ca_topic_score_gemma":0.000001407285,"domain_scores_codex":[0.996576,0.0004785368,0.001154411,0.001010716,0.0002359972,0.0005443828],"domain_scores_gemma":[0.9980795,0.00009500702,0.0003052872,0.000803367,0.000573541,0.0001433296],"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":[0.00005324216,0.0002751744,0.0005558376,0.0002543266,0.001387842,0.000009971177,0.00002547972,0.8738008,0.06435368,0.04691201,0.00007916812,0.01229244],"study_design_scores_gemma":[0.0006940539,0.0002611663,0.0001511509,0.0001471654,0.0005205571,0.00003511694,0.00002145026,0.6442072,0.005251063,0.3479174,0.000362946,0.0004306952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.212027,0.01018914,0.7736325,0.000285964,0.0001747475,0.0003032028,0.00002729917,0.00001159785,0.003348505],"genre_scores_gemma":[0.9886163,0.00009101731,0.01032545,0.000265337,0.00009065626,0.00002995092,0.0002292424,0.00004399437,0.00030804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7765893,"threshold_uncertainty_score":0.9997184,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414342823","doi":"10.5376/cmb.2024.14.0028","title":"Emerging Techniques in Biological Big Data Processing: From Algorithms to Applications","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Big data; Field (mathematics); Key (lock); Identification (biology)","retraction":null,"screen_n_in":null,"score":{"opus":0.05128017361932047,"gpt":0.3740622759055844,"spread":0.322782102286264,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003205676,0.0001663824,0.0001617594,0.0002029123,0.00005478597,0.00006225143,0.0006643727,0.0002388867,0.00001245578],"category_scores_gemma":[0.0001597061,0.0001435977,0.000048431,0.0003546383,0.0001842849,0.000004977399,0.0006914758,0.0001640034,0.00005559716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002284103,"about_ca_system_score_gemma":0.0002314444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002962134,"about_ca_topic_score_gemma":0.00001532205,"domain_scores_codex":[0.9984306,0.00007766267,0.0003544463,0.0006568299,0.0001641703,0.0003162787],"domain_scores_gemma":[0.999276,0.00005056548,0.00003432569,0.0003909352,0.0001058544,0.0001423371],"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":[0.00002375736,0.00008167734,0.0005108931,0.00003701492,0.00004707012,0.00001453824,0.00004813207,0.000148725,0.1915992,0.001182009,0.001082539,0.8052244],"study_design_scores_gemma":[0.0006443299,0.0009738303,0.003347092,0.0001666812,0.00003019559,0.00004544218,0.0001830263,0.05295254,0.04414355,0.1156827,0.7807609,0.001069715],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02236042,0.002753906,0.9720428,0.00152698,0.0001579302,0.0004304019,0.0002877622,0.00005628279,0.0003835097],"genre_scores_gemma":[0.9001661,0.0001516609,0.09098307,0.00106133,0.0005871573,0.0001551774,0.006833103,0.00002265798,0.00003972973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8810598,"threshold_uncertainty_score":0.5855743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417017349","doi":"10.5376/cmb.2025.15.0020","title":"Standardizing Bioinformatics Pipelines for Clinical Genomics","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Standardization; Workflow; Comparability; Genomics; Process (computing); Consistency (knowledge bases); Precision medicine; Quality (philosophy)","retraction":null,"screen_n_in":null,"score":{"opus":0.02152590597403535,"gpt":0.3836228400215117,"spread":0.3620969340474763,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006189542,0.0004427493,0.0005891573,0.0001945407,0.000309162,0.00009948073,0.000520109,0.0006627193,0.00001124988],"category_scores_gemma":[0.0006496215,0.000471248,0.0007346462,0.0001922801,0.0004690847,0.000005387764,0.0004456438,0.0001869506,0.00001403508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007102925,"about_ca_system_score_gemma":0.001753832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002812,"about_ca_topic_score_gemma":0.000002646993,"domain_scores_codex":[0.996985,0.0002087194,0.001336212,0.0008011375,0.0001081075,0.0005608094],"domain_scores_gemma":[0.9978556,0.0002372303,0.0003811004,0.0004887723,0.0008467877,0.0001905302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004083184,0.00205009,0.01249986,0.001814737,0.008112878,0.00006213,0.0001946871,0.08840273,0.0610009,0.4068423,0.05834414,0.3565923],"study_design_scores_gemma":[0.007735706,0.00172586,0.001552777,0.0001669107,0.0008226814,0.00005345884,0.0002837936,0.09203613,0.004153346,0.2622327,0.6275849,0.001651771],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0655226,0.01480749,0.9131176,0.001084478,0.002043122,0.0009153537,0.00123411,0.00001928754,0.001255972],"genre_scores_gemma":[0.9016479,0.00131053,0.0874544,0.00457643,0.0005577313,0.00007022642,0.004059462,0.00005326572,0.0002700069],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8361254,"threshold_uncertainty_score":0.9997739,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7108748659","doi":"10.5376/cmb.2025.15.0007","title":"Development and Market Prospect Analysis of Cordyceps Polysaccharides in the Pharmaceutical and Health Product Industries","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Fungal Biology and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Cordyceps; Product (mathematics); Industrialisation; Human health; Market analysis; New product development; Pharmaceutical industry; Sustainable development","retraction":null,"screen_n_in":null,"score":{"opus":0.03638502075150794,"gpt":0.3886257520438027,"spread":0.3522407312922947,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008940341,0.0001669369,0.0004921894,0.0004320599,0.0001430375,0.00001104229,0.0001183031,0.0001091082,0.00003022134],"category_scores_gemma":[0.0001642139,0.0001269895,0.00004206743,0.001378407,0.0006909649,0.00001359758,0.0001096597,0.0003066598,7.353561e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004900685,"about_ca_system_score_gemma":0.0006450443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003750402,"about_ca_topic_score_gemma":0.00002451154,"domain_scores_codex":[0.9983732,0.0003109976,0.0005535554,0.0004477956,0.00008676018,0.0002276721],"domain_scores_gemma":[0.9991431,0.0003977636,0.0001467162,0.0001506848,0.0001031452,0.00005862547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002924518,0.0004925617,0.6277499,0.0002674582,0.002708061,0.00001128828,0.001200881,0.0002119657,0.004960199,0.303932,0.0004040614,0.05776926],"study_design_scores_gemma":[0.0009134142,0.0001590501,0.9790817,0.00009214,0.0006371804,0.00001897449,0.0003671254,0.004485289,0.001673397,0.01076494,0.001668814,0.0001379106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377686,0.02055208,0.0127207,0.02747305,0.00008322689,0.001026012,0.00005487787,0.000009351931,0.0003120452],"genre_scores_gemma":[0.9935724,0.0002035184,0.003088519,0.002836526,0.00001545198,0.00006267241,0.0001812392,0.000003775794,0.00003594654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3513319,"threshold_uncertainty_score":0.517848,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417017348","doi":"10.5376/cmb.2025.15.0013","title":"Genomic Biomarker Discovery for Drug Sensitivity Using Omics Data","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Drug response; Omics; Precision medicine; Drug discovery; Drug; Personalized medicine; Drug development; Biomarker discovery; Biomarker","retraction":null,"screen_n_in":null,"score":{"opus":0.06434419681053444,"gpt":0.3904021325721642,"spread":0.3260579357616298,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002762127,0.0006726665,0.0008431863,0.0007004666,0.0005696106,0.0006645342,0.002300063,0.0003081847,0.000005452552],"category_scores_gemma":[0.0009456005,0.0007882689,0.0003893631,0.001193766,0.0006299711,0.000918682,0.004961927,0.0003498318,0.00001625453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004389016,"about_ca_system_score_gemma":0.003179671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001089371,"about_ca_topic_score_gemma":0.00001384354,"domain_scores_codex":[0.9930563,0.002061388,0.001161511,0.002500815,0.0003439768,0.0008760273],"domain_scores_gemma":[0.9924514,0.004359947,0.0005206913,0.001832552,0.0006719835,0.000163414],"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":[0.0001288482,0.0001976481,0.0004364061,0.0001122289,0.0005097072,0.00003112043,0.00006236845,0.5109158,0.01901775,0.4453088,0.0003421564,0.02293721],"study_design_scores_gemma":[0.0008103261,0.00003068724,0.002943792,0.00006058182,0.0001178673,0.00004404928,0.00001189603,0.6126593,0.0003672482,0.3819918,0.0005244251,0.0004380542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1120447,0.001951803,0.8767944,0.002746695,0.003492947,0.001132755,0.001635634,0.00007601002,0.0001250574],"genre_scores_gemma":[0.6010449,0.00001019547,0.3953412,0.001767103,0.0001315943,0.0000175743,0.001578174,0.0000359001,0.00007342204],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4890002,"threshold_uncertainty_score":0.9994568,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4251508342","doi":"10.7551/mitpress/2022.003.0014","title":"All You Need to Know about Molecular Biology","year":2000,"lang":"en","type":"book-chapter","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Need to know; Biology; Computational biology; Evolutionary biology; Computer science; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01364376535979959,"gpt":0.3015260635740791,"spread":0.2878822982142795,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003315206,0.0007887835,0.0007358813,0.0004742877,0.0001155939,0.00005983097,0.00102745,0.001628203,0.0004884779],"category_scores_gemma":[0.0002146877,0.0007827104,0.0004926676,0.000102842,0.0007016081,0.000003585287,0.000677976,0.0005428044,0.0009682132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007168532,"about_ca_system_score_gemma":0.0004925911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001991836,"about_ca_topic_score_gemma":0.000008886931,"domain_scores_codex":[0.9963483,0.0001390224,0.0009420857,0.001206324,0.0004257351,0.0009385652],"domain_scores_gemma":[0.9977173,0.00006403826,0.0002609826,0.0008267598,0.0004935384,0.0006374239],"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":[0.0007889228,0.0002992128,0.0001046135,0.0002438458,0.002378918,0.0002405279,0.0001288679,0.005193951,0.4223942,0.4022657,0.03884673,0.1271146],"study_design_scores_gemma":[0.0008559397,0.001369967,0.00003517688,0.0000780366,0.00008429776,0.00006604938,0.000006672309,0.0002956455,0.004846172,0.1914795,0.7998598,0.001022739],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007600062,0.01437458,0.5645483,0.006579213,0.001649626,0.002840818,0.001943997,0.0001833439,0.40028],"genre_scores_gemma":[0.4095362,0.009082006,0.1664033,0.07759193,0.005206232,0.0006080725,0.1062402,0.001446546,0.2238855],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.761013,"threshold_uncertainty_score":0.9998096,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4239626646","doi":"10.7551/mitpress/2022.003.0008","title":"Multiple Alignment","year":2000,"lang":"en","type":"book-chapter","venue":"Computational Molecular Biology","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.009032046004557877,"gpt":0.2221915059010773,"spread":0.2131594598965195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001667163,0.0002030234,0.0001743598,0.00008217803,0.00002848927,0.000007774553,0.00008820311,0.0002176967,0.0002031434],"category_scores_gemma":[0.000006146816,0.0002260005,0.00006375324,0.000008703571,0.00005346576,0.000009888206,0.00001988253,0.0001437807,0.0001559761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005668637,"about_ca_system_score_gemma":0.0000128763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.627395e-7,"about_ca_topic_score_gemma":4.052713e-7,"domain_scores_codex":[0.9994184,0.00000609059,0.0001794264,0.0001994834,0.00006814007,0.0001284027],"domain_scores_gemma":[0.9997209,0.00005445648,0.00003374834,0.0001212525,0.00002768865,0.00004195104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002413381,0.000002145187,3.599331e-7,0.000008288314,0.00004316058,0.000009192241,0.000003611759,0.7691221,0.000028591,0.2262248,0.0001753051,0.004380067],"study_design_scores_gemma":[0.0001643061,0.00002623081,0.000004113018,0.0000168734,0.00001754918,0.000008241573,2.387182e-7,0.0710453,0.00009336416,0.8743291,0.05403322,0.000261416],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000009937759,0.0005227465,0.7902405,0.00001548531,0.0001795435,0.0000948766,0.00005985177,0.0001696115,0.2087074],"genre_scores_gemma":[0.7653182,0.0005311059,0.1852027,0.0006449014,0.0004126744,0.00004795357,0.009184339,0.0003359375,0.03832225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7653082,"threshold_uncertainty_score":0.9216033,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414316140","doi":"10.5376/cmb.2024.14.0027","title":"Off-Target Effects in Genome Editing: A Systematic Review of Prediction and Mitigation Strategies","year":2024,"lang":"en","type":"review","venue":"Computational Molecular Biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Genome; Genomics; Human genome; Context (archaeology); Identification (biology)","retraction":null,"screen_n_in":null,"score":{"opus":0.006641624782293781,"gpt":0.3396840854121475,"spread":0.3330424606298537,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002551911,0.0002688637,0.000959056,0.0001647362,0.00001388799,0.00001419723,0.0001208694,0.0002586718,0.000001514313],"category_scores_gemma":[0.0001690694,0.0002324922,0.0002017395,0.00016818,0.00006150025,0.000002469516,0.0000945161,0.000139734,0.000003000918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000209708,"about_ca_system_score_gemma":0.0001517547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001096927,"about_ca_topic_score_gemma":6.001874e-7,"domain_scores_codex":[0.9984912,0.0002196816,0.0006606372,0.0003916846,0.00008909923,0.0001476605],"domain_scores_gemma":[0.9994594,0.00005418756,0.0002012684,0.0001744481,0.00007111184,0.00003960662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000001103802,0.00001394369,0.000001389645,0.9940532,0.0002217243,0.000008812764,0.00001036684,0.000335982,0.0006635667,0.002019324,0.00003017664,0.002640369],"study_design_scores_gemma":[0.0004416879,0.0006550251,0.00002557404,0.9541787,0.00299462,0.0003796958,0.00002970534,0.00135245,0.0001377248,0.01490624,0.02407437,0.0008241952],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00003525823,0.9250456,0.07358937,0.00001110042,0.0001609018,0.001009397,0.0000939306,0.00001099762,0.00004342014],"genre_scores_gemma":[0.00172263,0.9937063,0.001589497,0.00004751389,0.00009768154,0.0002206511,0.002574591,0.00003333666,0.000007763485],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.07199987,"threshold_uncertainty_score":0.9480756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414316297","doi":"10.5376/cmb.2024.14.0026","title":"Exploring Molecular Interactions: The Role of Biophysics in Understanding Biological Processes","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Mechanism (biology); Context (archaeology)","retraction":null,"screen_n_in":null,"score":{"opus":0.09318341347914025,"gpt":0.3393424604508636,"spread":0.2461590469717233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002437192,0.0001463229,0.0001493176,0.0001552613,0.00004938752,0.00003226644,0.0002735351,0.0001017002,0.000008700303],"category_scores_gemma":[0.0002933311,0.0001049296,0.00009461302,0.0004001969,0.0003584898,0.000009257334,0.0001884126,0.0001752819,0.00001148923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003692032,"about_ca_system_score_gemma":0.0002068235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001246889,"about_ca_topic_score_gemma":0.000006837646,"domain_scores_codex":[0.9988393,0.0001074169,0.0003373332,0.0002939184,0.0001628529,0.0002592049],"domain_scores_gemma":[0.9994652,0.0001411997,0.0000539334,0.0001638798,0.0001184071,0.00005738474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006844474,0.0001146306,0.0009749556,0.0001621466,0.0001641456,0.00001865036,0.0002740734,0.003011301,0.8677113,0.1113707,0.00004353291,0.01608605],"study_design_scores_gemma":[0.000568717,0.0009133865,0.001035707,0.0002265575,0.00002961439,0.00006518466,0.001828861,0.01567558,0.2760568,0.6925706,0.01050791,0.0005210017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6107471,0.004617678,0.3825192,0.0006392048,0.0002607091,0.0002817802,0.00003638059,0.0000229357,0.000874994],"genre_scores_gemma":[0.9975764,0.0003476504,0.001571218,0.0001374883,0.00007646043,0.00005012448,0.0002208478,0.00001373095,0.000006136725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5916545,"threshold_uncertainty_score":0.4278906,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4229621779","doi":"10.7551/mitpress/2022.003.0013","title":"Problems","year":2000,"lang":"en","type":"book-chapter","venue":"Computational Molecular Biology","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01714433553333848,"gpt":0.2671787649691041,"spread":0.2500344294357656,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001042865,0.0004695805,0.0004677143,0.0003468111,0.00006921058,0.0000223452,0.0003494786,0.0006087941,0.003050156],"category_scores_gemma":[0.0000159142,0.0005008669,0.0002561948,0.00004395531,0.0003566797,0.00002135309,0.0001047831,0.0004108697,0.02242044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001127169,"about_ca_system_score_gemma":0.0002096686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004651884,"about_ca_topic_score_gemma":0.000002145402,"domain_scores_codex":[0.9982011,0.00006223112,0.0004522268,0.0007230776,0.0002420498,0.0003193027],"domain_scores_gemma":[0.9990236,0.00009193482,0.0002479165,0.0003236749,0.0001986128,0.0001142721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001998237,0.00001852809,0.000003471723,0.00001334128,0.0002196195,0.00007058712,0.000011036,0.01860329,0.000704964,0.973759,0.00212453,0.004451628],"study_design_scores_gemma":[0.0002600126,0.0000889915,0.00001289663,0.00004215337,0.00004184045,0.00007954241,1.285377e-7,0.000487443,0.000007333175,0.7439291,0.2546733,0.0003772154],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00006415341,0.002071122,0.04112332,0.0001686159,0.0001948308,0.0005157322,0.0005337604,0.000268887,0.9550596],"genre_scores_gemma":[0.2116023,0.0001325301,0.046268,0.0065899,0.001452485,0.0002437503,0.05377039,0.002254351,0.6776863],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2773733,"threshold_uncertainty_score":0.9997443,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4245845951","doi":"10.7551/mitpress/2022.003.0009","title":"Finding Signals in DNA","year":2000,"lang":"en","type":"book-chapter","venue":"Computational Molecular Biology","topic":"DNA and Nucleic Acid Chemistry","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computational biology; Computer science; Biology; Evolutionary biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01038220439665168,"gpt":0.2602321798450498,"spread":0.2498499754483981,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009009067,0.0003561441,0.0003304745,0.0001213043,0.00004094645,0.00001445132,0.0003047355,0.0007719365,0.0004989164],"category_scores_gemma":[0.0000248594,0.0004004019,0.0001965041,0.00002985496,0.0001725,0.000001544909,0.0001380334,0.0002534018,0.0001199613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003349353,"about_ca_system_score_gemma":0.0001463226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000220621,"about_ca_topic_score_gemma":0.000002303112,"domain_scores_codex":[0.9985193,0.00003185635,0.0003822536,0.0006582518,0.0001235899,0.000284755],"domain_scores_gemma":[0.999395,0.00002667961,0.0001424294,0.000289173,0.00006193855,0.00008485482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002459324,0.0001064955,0.000202798,0.00008002601,0.0004641329,0.0002919338,0.00002969699,0.009058174,0.8583774,0.1109025,0.004470284,0.01577064],"study_design_scores_gemma":[0.001283525,0.0003645964,0.0001101598,0.0001582586,0.0000580507,0.0001497732,0.000004585559,0.0002386587,0.03605432,0.7399135,0.2203453,0.001319208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1082494,0.004464741,0.01409771,0.0003302007,0.0003079924,0.0004590039,0.0002647499,0.00005148458,0.8717747],"genre_scores_gemma":[0.9270771,0.0002136434,0.002286977,0.001515712,0.0004561354,0.00002733607,0.006317025,0.0001466283,0.06195946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8223231,"threshold_uncertainty_score":0.9998448,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414316292","doi":"10.5376/cmb.2024.14.0030","title":"The Role of High-Performance Computing in Modern Biology: Tackling Big Data Challenges","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Big data; Key (lock); Field (mathematics); Work (physics); Variety (cybernetics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03269983061506715,"gpt":0.3174019860424184,"spread":0.2847021554273512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007058469,0.0001558659,0.0001822836,0.0001219825,0.00008217882,0.00002842691,0.0007293737,0.0002078309,0.000001428981],"category_scores_gemma":[0.0002212225,0.0001165649,0.00004866966,0.000154299,0.0004184129,0.000004841997,0.0007099594,0.0001884284,0.000009028913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001434029,"about_ca_system_score_gemma":0.0002159546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002138754,"about_ca_topic_score_gemma":0.00002119094,"domain_scores_codex":[0.9984897,0.0001345048,0.0004298637,0.0004338969,0.0001520536,0.000360002],"domain_scores_gemma":[0.9991247,0.0001695777,0.00007403787,0.0004483228,0.0001194803,0.00006386657],"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":[0.00004770389,0.00005005765,0.0009935066,0.0001074039,0.0001335005,0.000003568372,0.0001019418,0.005400016,0.113626,0.01578199,0.00004730602,0.8637071],"study_design_scores_gemma":[0.0007424245,0.0007754757,0.004584929,0.0001419521,0.00002270807,0.00003546783,0.0002605199,0.7633982,0.03040406,0.1800489,0.01913483,0.0004505316],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7563937,0.04032267,0.2009528,0.0008033504,0.0005328754,0.0002453219,0.0001025767,0.0000227876,0.0006238869],"genre_scores_gemma":[0.9928911,0.002053227,0.003790439,0.00008193108,0.0002227716,0.000006632097,0.0009256843,0.00001640044,0.00001183972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8632565,"threshold_uncertainty_score":0.475338,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1537397732","doi":"10.5376/cmb.2013.03.0002","title":"Identification of the <i>Bona fide</i> Differentially Methylated Gene Markers among Cancers","year":2013,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Identification (biology); Gene; Genetics; Biology; Molecular biology","retraction":null,"screen_n_in":null,"score":{"opus":0.005696133340438093,"gpt":0.240251173054191,"spread":0.2345550397137529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002328228,0.0002073264,0.0002050579,0.0000844783,0.0001116615,0.00002760879,0.000363602,0.0002506967,0.00004518823],"category_scores_gemma":[0.0001685843,0.0001763885,0.0001899823,0.0002036008,0.0003065868,0.000006286119,0.0001710678,0.00008791251,0.0000144794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004085226,"about_ca_system_score_gemma":0.0001590621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003580901,"about_ca_topic_score_gemma":0.00002837689,"domain_scores_codex":[0.9982544,0.0003182562,0.0005257889,0.0004498007,0.000201031,0.0002507676],"domain_scores_gemma":[0.9986691,0.00004910683,0.0003708035,0.0004049214,0.0004268417,0.0000791534],"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":[0.00002006257,0.00003141302,0.002654033,0.00001039307,0.0001362961,4.221029e-7,0.00001938016,0.01570654,0.977907,0.001838792,0.0001454885,0.001530162],"study_design_scores_gemma":[0.0006197226,0.0001771626,0.2629213,0.00001269088,0.00006122606,0.000002874804,0.000007082917,0.009538502,0.6908699,0.03479132,0.0006850255,0.0003132055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8344511,0.001424671,0.1629782,0.0001754029,0.000312172,0.0003983305,0.00003455327,0.00001106004,0.0002145675],"genre_scores_gemma":[0.9969345,0.00006302774,0.001806781,0.0001066648,0.00007098479,0.00006659763,0.0007354702,0.00002836986,0.0001876192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2870371,"threshold_uncertainty_score":0.7192915,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405859870","doi":"10.5376/cmb.2024.14.0011","title":"Embracing AI's Role in Structural Biology While Recognizing Its Limits","year":2024,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Biology; Environmental ethics; Evolutionary biology; Philosophy","retraction":null,"screen_n_in":null,"score":{"opus":0.01939552932041826,"gpt":0.3224797935742129,"spread":0.3030842642537946,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003248966,0.0002293685,0.0002256957,0.0002756616,0.0000755362,0.00006619233,0.0003109465,0.0003702766,0.00004458609],"category_scores_gemma":[0.0002961429,0.0002094811,0.0001219645,0.0002820971,0.0001986428,0.000009155096,0.0002213058,0.0003000639,0.00009968324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003706329,"about_ca_system_score_gemma":0.0002668714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000158671,"about_ca_topic_score_gemma":0.00002109171,"domain_scores_codex":[0.9981706,0.000168397,0.0004431561,0.0005472567,0.0001497554,0.0005208368],"domain_scores_gemma":[0.9993542,0.00008187167,0.00005261943,0.0001890602,0.0001748417,0.0001473369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001021674,0.00004749107,0.001350112,0.0001479365,0.0001688505,0.00003993197,0.0001700866,0.00274627,0.8969116,0.03031349,0.0005010801,0.06750101],"study_design_scores_gemma":[0.002494863,0.002334935,0.007975545,0.0002859181,0.0000592986,0.000334283,0.0002843411,0.2207981,0.1697113,0.5436099,0.05030124,0.001810217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8733152,0.01164946,0.1047143,0.002060233,0.001399873,0.0005411605,0.0001604234,0.00008119131,0.006078225],"genre_scores_gemma":[0.9936467,0.0000759724,0.003583931,0.00093406,0.0003159907,0.00002515334,0.001293128,0.00002705848,0.00009804487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7272003,"threshold_uncertainty_score":0.8542392,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417022542","doi":"10.5376/cmb.2025.15.0012","title":"Building an Integrated Multi-Omics Database for Rare Diseases","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Disease; Rare disease; Data integration; MEDLINE; Drug development","retraction":null,"screen_n_in":null,"score":{"opus":0.01312373749524518,"gpt":0.3254323490059796,"spread":0.3123086115107344,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001694646,0.0005347852,0.0004693801,0.0002566603,0.000356168,0.0001148533,0.0006645507,0.0004475804,0.00001788748],"category_scores_gemma":[0.000493988,0.0005709308,0.0004053306,0.0002700598,0.0003741337,0.00001159452,0.0004353141,0.0001689545,0.000005252114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007546939,"about_ca_system_score_gemma":0.001274257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002249707,"about_ca_topic_score_gemma":0.00001037216,"domain_scores_codex":[0.9970713,0.0002941902,0.0006296617,0.001307795,0.00009832471,0.0005987593],"domain_scores_gemma":[0.998001,0.0001155021,0.0002219946,0.0006301778,0.0007270954,0.0003042201],"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":[0.002148711,0.003049984,0.003395857,0.000638099,0.002214753,0.0001081209,0.0000499864,0.07903562,0.6152486,0.2357626,0.002457436,0.05589025],"study_design_scores_gemma":[0.0225399,0.003561645,0.00550595,0.0005457168,0.002375838,0.0001411792,0.0006512267,0.4480058,0.05261291,0.2692026,0.1900348,0.004822396],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.143895,0.01067828,0.8364009,0.0003611667,0.0008674272,0.0008830169,0.006854585,0.00002857627,0.0000310692],"genre_scores_gemma":[0.8523288,0.0001293988,0.1146479,0.002036171,0.0001861564,0.0001443464,0.03036624,0.00005746045,0.0001035229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7217529,"threshold_uncertainty_score":0.9996742,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}