{"id":"W2915975108","doi":"10.1093/nar/gku1204","title":"The BioGRID interaction database: 2015 update","year":2014,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":925,"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; National Institutes of Health","keywords":"Biology; Data curation; Database; Model organism; Computational biology; Bioinformatics; World Wide Web; Computer science; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001849595,0.00009369318,0.00007258135,0.00004692926,0.0004257689,0.0001376843,0.0004610476,0.0001104063,0.00005386268],"category_scores_gemma":[0.0001557625,0.00006491791,0.00005006908,0.0001362148,0.0002078218,0.000007646254,0.0004180583,0.0003325563,0.0003983888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001848334,"about_ca_system_score_gemma":0.00004441854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001902264,"about_ca_topic_score_gemma":0.00004444323,"domain_scores_codex":[0.9987546,0.0001629591,0.0001831952,0.0002099077,0.0002723592,0.0004169662],"domain_scores_gemma":[0.9989732,0.00005585338,0.00004296041,0.0006659179,0.0001595483,0.0001025098],"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":[0.0002353651,0.00004955796,0.0003554088,0.00002362896,0.00007426121,0.000001194253,0.00006262589,0.00006036856,0.09461316,0.005110597,0.7521913,0.1472225],"study_design_scores_gemma":[0.0002124572,0.0001460791,0.0002267083,0.000008658868,0.000002677176,0.000007808285,0.0001166487,0.005750442,0.006125271,0.0004616791,0.9868475,0.00009404829],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7784451,0.00613919,0.05215617,0.02194582,0.003687844,0.001801975,0.0001612683,0.0001267623,0.1355359],"genre_scores_gemma":[0.9945464,0.001300561,0.0008516412,0.0003219796,0.0008522177,0.00002339733,0.0002147172,0.00002381701,0.001865255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2346562,"threshold_uncertainty_score":0.5120615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02568691385085878,"score_gpt":0.3375835671002954,"score_spread":0.3118966532494366,"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."}}