{"id":"W2099982459","doi":"10.1126/science.1064987","title":"A Combined Experimental and Computational Strategy to Define Protein Interaction Networks for Peptide Recognition Modules","year":2002,"lang":"en","type":"article","venue":"Science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":713,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"National Center for Research Resources","keywords":"Protein–protein interaction; Computational biology; Phage display; Two-hybrid screening; Biology; Immunoprecipitation; Peptide; Interaction network; Computer science; Yeast; Genetics; Gene; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110202,0.001061108,0.0006819984,0.001395462,0.0006782289,0.000962704,0.001178561,0.00072622,0.001419561],"category_scores_gemma":[0.004878805,0.0005487182,0.001120423,0.001072645,0.0009067924,0.001446627,0.0006999696,0.001009353,0.0001957184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009443757,"about_ca_system_score_gemma":0.001382577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132257,"about_ca_topic_score_gemma":0.003947131,"domain_scores_codex":[0.9995577,0.0002005021,0.00002794918,0.00008851645,0.00009902354,0.00002627053],"domain_scores_gemma":[0.9981673,0.001318865,0.0001510958,0.0001863798,0.000118251,0.00005810309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001529506,0.0002687116,0.005629262,0.0003551481,0.0002291087,0.000190236,0.00004966054,0.889658,0.01578042,0.05963975,0.0007697974,0.02727692],"study_design_scores_gemma":[0.00002103179,0.00003059209,0.0004136499,0.000005676545,0.00002185903,0.00002668948,0.00001481963,0.975058,0.002753173,0.02102015,0.0006266311,0.000007766721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1491723,0.0002937036,0.8440277,0.000525868,0.0000315449,0.0002825735,0.001820426,0.001301166,0.00254472],"genre_scores_gemma":[0.5178192,0.0003170868,0.4767486,0.0001241187,0.00003024707,0.001027289,0.003390789,0.0001102039,0.0004324338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002132257,"threshold_uncertainty_score":0.006851971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255673608736219,"score_gpt":0.2599834258017361,"score_spread":0.2374266897143739,"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."}}