{"id":"W2177550115","doi":"10.1093/bioinformatics/btv597","title":"PDID: database of molecular-level putative protein–drug interactions in the structural human proteome","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institutes of Health; U.S. National Library of Medicine; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Proteome; Human proteome project; Computational biology; Drug; Computer science; Database; Bioinformatics; Biology; Proteomics; Genetics; Pharmacology; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001101826,0.002226718,0.002259128,0.004434313,0.001139777,0.002121089,0.002359988,0.001536694,0.02717695],"category_scores_gemma":[0.003818896,0.0009233957,0.0009810275,0.006736485,0.0003348166,0.001905982,0.001865605,0.001280556,0.01648633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170313,"about_ca_system_score_gemma":0.003098019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001595726,"about_ca_topic_score_gemma":0.002061232,"domain_scores_codex":[0.9993242,0.0001105073,0.0001093333,0.0002078066,0.0001931013,0.00005504998],"domain_scores_gemma":[0.998563,0.0005197382,0.0003339719,0.0002271755,0.0001659685,0.0001901097],"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":[0.004183339,0.0003508092,0.02093216,0.01166068,0.0005948187,0.001759896,0.0002575046,0.01086992,0.02459442,0.01358685,0.8268862,0.08432356],"study_design_scores_gemma":[0.001458248,0.0005825621,0.03531088,0.0009407821,0.0007851692,0.004258014,0.0002747684,0.03462228,0.02346458,0.0204124,0.8776845,0.0002059627],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0159218,0.003864946,0.01744184,0.0005184471,0.00008135905,0.000221032,0.9368392,0.01865808,0.006453284],"genre_scores_gemma":[0.02141226,0.00145512,0.01733577,0.0001518945,0.00002866905,0.0002284572,0.9582772,0.000457215,0.0006533631],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02717695,"threshold_uncertainty_score":0.09091592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03435201254339759,"score_gpt":0.2947702654347224,"score_spread":0.2604182528913248,"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."}}