{"id":"W2915536738","doi":"10.1093/nar/gkw1102","title":"The BioGRID interaction database: 2017 update","year":2016,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1022,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier de l'Université Laval; Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute; Université de Montréal; Institute for Research in Immunology and Cancer","funders":"National Heart, Lung, and Blood Institute; Biotechnology and Biological Sciences Research Council; Ontario Genomics Institute; Ontario Genomics; Genome Canada; National Institutes of Health; Harvard University","keywords":"DrugBank; Biology; Database; Model organism; Drug discovery; Computational biology; Annotation; Bioinformatics; Computer science; Drug; Genetics; Pharmacology; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002485217,0.002362978,0.002419297,0.006815403,0.0008429949,0.004594125,0.004494235,0.001700275,0.03471743],"category_scores_gemma":[0.009005812,0.001232575,0.001501973,0.01150558,0.0004192791,0.003781044,0.004255876,0.002290926,0.04919665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002256323,"about_ca_system_score_gemma":0.004887801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01589963,"about_ca_topic_score_gemma":0.01561191,"domain_scores_codex":[0.9983199,0.0002526654,0.000389192,0.0002093182,0.0006873913,0.000141526],"domain_scores_gemma":[0.9968376,0.000579109,0.0004055641,0.0007835882,0.0009326494,0.0004614467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002259154,0.00002509674,0.0007853688,0.0009984554,0.0001059626,0.00006769847,0.00003821044,0.0008671918,0.0003435632,0.002595754,0.9493992,0.04454747],"study_design_scores_gemma":[0.00005612005,0.000005637482,0.0009548004,0.0002612117,0.000054139,0.0001013117,0.00001738576,0.0004466975,0.0002840815,0.002300725,0.9954888,0.00002895242],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.002393705,0.01810717,0.01548092,0.004662791,0.001511822,0.0002305347,0.9027355,0.03321963,0.02165792],"genre_scores_gemma":[0.003617208,0.0081803,0.007128108,0.0009348585,0.0002270117,0.0003108035,0.9719912,0.003050576,0.004559903],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03471743,"threshold_uncertainty_score":0.1161413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03685547101464673,"score_gpt":0.3420218210836367,"score_spread":0.30516635006899,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}