{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001171559,0.00009150045,0.00006508164,0.00004592887,0.0004111289,0.00009642745,0.0004917418,0.0001070366,0.00008658594],"category_scores_gemma":[0.0001363182,0.00004837251,0.00005293636,0.0001017011,0.0002787905,0.00001085236,0.0004517786,0.0001935962,0.0005855353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002658282,"about_ca_system_score_gemma":0.00006324235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001096926,"about_ca_topic_score_gemma":0.00003538186,"domain_scores_codex":[0.9988115,0.0001034974,0.0001769677,0.000217909,0.0002543013,0.0004358489],"domain_scores_gemma":[0.9989405,0.00006389163,0.00004497881,0.0007001331,0.0001536858,0.00009680328],"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":[0.0002220945,0.00002873379,0.0003212269,0.000009282716,0.00006063604,0.000003087121,0.00002684509,0.000001815352,0.3388651,0.002026362,0.4788145,0.1796203],"study_design_scores_gemma":[0.0002988681,0.0001190124,0.0003703926,0.00002054163,0.000002435225,0.00001393062,0.00008808682,0.000193751,0.02098911,0.0004690636,0.9773363,0.00009846395],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8752093,0.005171379,0.02465636,0.02569862,0.002572373,0.001350084,0.0003213273,0.00008395075,0.06493663],"genre_scores_gemma":[0.9896315,0.004661859,0.0003865786,0.0001683782,0.0006431616,0.00002709376,0.00007063144,0.00002092651,0.004389902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4985218,"threshold_uncertainty_score":0.7526067,"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."}}