{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001232164,0.00006035198,0.00004599216,0.00002871751,0.0001284482,0.00005988125,0.00007008886,0.00003130562,0.00001032098],"category_scores_gemma":[0.00001452799,0.0000583367,0.00001591013,0.00007076532,0.0000842673,0.00001176983,0.00004722451,0.00002899054,0.000004508258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001174803,"about_ca_system_score_gemma":0.00001062557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002160749,"about_ca_topic_score_gemma":0.000003467346,"domain_scores_codex":[0.9995247,0.000005514439,0.0001039864,0.0001651911,0.00006341385,0.0001372423],"domain_scores_gemma":[0.9997699,0.000004963765,0.00003752831,0.00006642572,0.00005711276,0.00006402141],"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":[0.0002634144,0.0001960595,0.0001100643,0.00002769414,0.00002171275,6.198554e-7,0.0003856197,0.05458367,0.7781886,0.001226482,0.003787843,0.1612082],"study_design_scores_gemma":[0.0009225139,0.001745247,0.0009356406,0.00003881262,0.000004956896,0.00001670726,0.0003094942,0.9339404,0.05824226,0.001898949,0.001662576,0.0002824954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.920283,0.000132426,0.07867192,0.00008387815,0.00007605954,0.0003373847,0.000008348935,0.000005820982,0.0004011743],"genre_scores_gemma":[0.9894145,0.000004957634,0.01014161,0.0001787595,0.00006624191,0.00004859846,0.0000419495,0.000004030781,0.0000993341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8793567,"threshold_uncertainty_score":0.2378902,"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."}}