{"id":"W3093053585","doi":"10.1002/pro.3978","title":"The <scp>BioGRID</scp> database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions","year":2020,"lang":"en","type":"article","venue":"Protein Science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1948,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Research in Immunology and Cancer; Hospital for Sick Children; Université de Montréal; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"National Center for Advancing Translational Sciences; Canadian Institutes of Health Research; National Institutes of Health; Stand Up To Cancer; Genome Canada","keywords":"Resource (disambiguation); Database; Computational biology; Chemistry; Biology; World Wide Web; Computer science; Computer network","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":[0.001984161,0.004069852,0.004178455,0.009126142,0.001926814,0.004198775,0.00496173,0.002687137,0.05463694],"category_scores_gemma":[0.005584859,0.001288796,0.002282581,0.01614049,0.00074955,0.002505908,0.005599658,0.002693102,0.05365725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001978394,"about_ca_system_score_gemma":0.00525958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01085262,"about_ca_topic_score_gemma":0.01724049,"domain_scores_codex":[0.9979243,0.0003189832,0.0003541959,0.0005732884,0.0005847706,0.0002444444],"domain_scores_gemma":[0.9964066,0.000777156,0.0006344139,0.0008738695,0.0006413097,0.000666648],"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":[0.000451017,0.00004182177,0.001356978,0.008236201,0.0003788369,0.0004222691,0.0001246569,0.001270503,0.008720348,0.004272594,0.9630129,0.01171187],"study_design_scores_gemma":[0.0003314076,0.00005231937,0.006087445,0.001034021,0.0002585188,0.0004992337,0.0001004416,0.001854572,0.006978405,0.008378083,0.9742784,0.0001472058],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007403938,0.001289514,0.002654704,0.0002082893,0.00008299006,0.00006443942,0.9848354,0.007320608,0.002803752],"genre_scores_gemma":[0.001693261,0.0009137965,0.003659661,0.0001911079,0.000020297,0.0001642082,0.9916932,0.001058154,0.0006062777],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05463694,"threshold_uncertainty_score":0.1827788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474813255882454,"score_gpt":0.2517007002253696,"score_spread":0.2369525676665451,"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."}}