{"id":"W2161701113","doi":"10.1186/1471-2105-7-365","title":"PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs","year":2006,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tandem affinity purification; Saccharomyces cerevisiae; Protein–protein interaction; Yeast; Computational biology; Interaction network; Biology; Gene; Genetics; Bioinformatics; Biochemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008058713,0.0003676527,0.0002462905,0.0001264887,0.0003113109,0.0002035388,0.0003708586,0.0002663024,0.00003120056],"category_scores_gemma":[0.0001753324,0.0002762733,0.0001812866,0.0002130822,0.00009738868,0.00005979853,0.000139623,0.0004568099,0.00003740944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009290737,"about_ca_system_score_gemma":0.0001329232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006077559,"about_ca_topic_score_gemma":0.00008204162,"domain_scores_codex":[0.9979101,0.00008748876,0.0009341653,0.0002558326,0.0003406329,0.0004717679],"domain_scores_gemma":[0.9987123,0.00006697648,0.0004081447,0.0006060833,0.000112466,0.00009400296],"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":[0.002413335,0.00117986,0.02866225,0.004995169,0.000965056,0.00001559609,0.002659531,0.1758518,0.5675234,0.00365718,0.02790008,0.1841768],"study_design_scores_gemma":[0.0007783776,0.0008389488,0.0006610623,0.00121714,0.00005908615,0.00001341424,0.001430069,0.7444789,0.227973,0.0002565743,0.02152647,0.000766941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7094641,0.00007622402,0.2764006,0.0003447693,0.0003113393,0.002711278,0.0001383105,0.000142916,0.01041045],"genre_scores_gemma":[0.9700279,0.000002286897,0.02803907,0.0001873544,0.0007360983,0.0002334018,0.0004105747,0.00003604841,0.0003272371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5686271,"threshold_uncertainty_score":0.9999689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01760215492765312,"score_gpt":0.2389834037510581,"score_spread":0.221381248823405,"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."}}