{"id":"W1999586982","doi":"10.1038/nmeth.3178","title":"In silico prediction of physical protein interactions and characterization of interactome orphans","year":2014,"lang":"en","type":"article","venue":"Nature Methods","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":165,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute; University Health Network; Princess Margaret Cancer Centre","funders":"National Cancer Institute; National Human Genome Research Institute; Canadian Institutes of Health Research","keywords":"Interactome; In silico; Proteome; Computational biology; Protein–protein interaction; Computer science; Proteomics; Drug discovery; Human proteome project; Function (biology); Human proteins; Protein function; Biology; Bioinformatics; Genetics","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":[0.0008082861,0.001152524,0.001381025,0.0020961,0.0009735168,0.001173912,0.001177148,0.0008864166,0.006779598],"category_scores_gemma":[0.002974357,0.0005812692,0.00265292,0.001167192,0.0004223313,0.0009133519,0.000937939,0.000818509,0.001516347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005697563,"about_ca_system_score_gemma":0.0009688547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001307682,"about_ca_topic_score_gemma":0.003655559,"domain_scores_codex":[0.9994748,0.0001563982,0.00004662951,0.0001843167,0.00008262855,0.00005522198],"domain_scores_gemma":[0.9985108,0.000950565,0.0001657703,0.0001994295,0.00008860372,0.00008479617],"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":[0.01252108,0.002245368,0.2022281,0.006010365,0.003765268,0.005746132,0.0008356977,0.234142,0.3486065,0.03006906,0.02046899,0.1333615],"study_design_scores_gemma":[0.0003805707,0.0003913127,0.02827196,0.00008704794,0.000883255,0.001638997,0.0001775927,0.9006178,0.04708451,0.01206972,0.008332029,0.0000651622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7428505,0.001259935,0.2166845,0.0005122367,0.0001071659,0.0002560866,0.02335956,0.01037481,0.004595155],"genre_scores_gemma":[0.7852294,0.0005902866,0.1702551,0.0001802576,0.00003592382,0.0003531849,0.04114569,0.000687574,0.001522451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006779598,"threshold_uncertainty_score":0.02268004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005741715191159567,"score_gpt":0.3065507368747328,"score_spread":0.3008090216835732,"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."}}