{"id":"W2155653061","doi":"10.1093/nar/gkn870","title":"PIPs: human protein-protein interaction prediction database","year":2008,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Computer science; Bayesian network; Interaction network; Protein–protein interaction; Domain (mathematical analysis); Interaction information; Database; Data mining; The Internet; Computational biology; Resource (disambiguation); Network topology; Biology; Machine learning; Gene; Genetics; Mathematics; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000670431,0.001893458,0.002116478,0.002280026,0.000802418,0.001256852,0.001809216,0.001046054,0.05321451],"category_scores_gemma":[0.001592532,0.000675389,0.001220497,0.004291594,0.000208695,0.001130767,0.001333867,0.001170922,0.04147116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005866309,"about_ca_system_score_gemma":0.001989827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002470553,"about_ca_topic_score_gemma":0.002631843,"domain_scores_codex":[0.9996386,0.00005877747,0.00005663782,0.00009743626,0.00009512898,0.000053503],"domain_scores_gemma":[0.9996611,0.00008652103,0.00004968864,0.00006233469,0.00006547989,0.00007493022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001571232,0.0001079442,0.003563593,0.004725105,0.0002543695,0.001056283,0.0001000751,0.003287379,0.01270561,0.004444669,0.9134793,0.05470451],"study_design_scores_gemma":[0.0007095112,0.0002381804,0.01681175,0.0005884251,0.0004290362,0.002903429,0.0001277306,0.01778345,0.01073071,0.01401147,0.9354959,0.0001705404],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.007271109,0.004072101,0.01641371,0.0004999939,0.0001559615,0.0002362166,0.9451773,0.01668645,0.009487222],"genre_scores_gemma":[0.0137282,0.001728274,0.01712508,0.0001983826,0.00003551371,0.0003782913,0.9641632,0.0005236585,0.002119363],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05321451,"threshold_uncertainty_score":0.1780203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05341585441553909,"score_gpt":0.3347498492448496,"score_spread":0.2813339948293105,"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."}}