{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002837122,0.00006393988,0.0001260268,0.00005221669,0.000009881637,0.000003976597,0.00005184221,0.0001410756,0.000003897569],"category_scores_gemma":[0.0001154702,0.00005623534,0.00003377957,0.0000747076,0.00003333767,0.000007982226,0.00004108152,0.0001722627,1.754273e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004504781,"about_ca_system_score_gemma":0.00001197352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002860288,"about_ca_topic_score_gemma":0.000002829149,"domain_scores_codex":[0.9995158,0.0001010333,0.0001798861,0.00009874312,0.00004089735,0.0000636619],"domain_scores_gemma":[0.9996592,0.00002321267,0.0001190409,0.0001263844,0.00004756513,0.00002462064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000351362,0.0000369473,0.000248485,0.00003714565,0.0000116114,2.477336e-8,0.000141954,0.000005850787,0.97113,0.0004375526,0.00001736662,0.02789794],"study_design_scores_gemma":[0.0003018939,0.00021786,0.007631268,0.00004969483,0.00001047763,0.000003364269,0.0000170107,0.009660807,0.9657591,0.0006581164,0.01562526,0.00006513293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9297047,0.00004015954,0.06959016,0.00007220991,0.0001295601,0.0001322162,0.0000197548,0.000002300343,0.0003089311],"genre_scores_gemma":[0.9881921,0.00001020109,0.01142381,0.00006958497,0.0001381254,0.000009021026,0.00006642831,0.000005932498,0.00008480842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05848737,"threshold_uncertainty_score":0.229321,"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."}}