{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004721743,0.0001695115,0.0001611699,0.00007444989,0.00006439214,0.00004128856,0.0003870835,0.00004872708,0.000005956805],"category_scores_gemma":[0.00006935063,0.0001212475,0.00007053541,0.0001604008,0.0001154845,0.00002967576,0.0001712902,0.0001935727,0.00000957365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002287223,"about_ca_system_score_gemma":0.0001144037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006682926,"about_ca_topic_score_gemma":0.0001119749,"domain_scores_codex":[0.9988289,0.00005452111,0.0005511605,0.0001049994,0.0002281076,0.0002323019],"domain_scores_gemma":[0.9990784,0.00001052922,0.0002822159,0.000439336,0.0001129478,0.0000765681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001098815,0.001019929,0.004174334,0.002528844,0.001007669,0.00006246281,0.08401567,0.009930238,0.7666538,0.04255238,0.05382473,0.03313107],"study_design_scores_gemma":[0.01354386,0.00226605,0.007238229,0.0009275365,0.0002646569,0.0005723258,0.06460623,0.1116324,0.7282877,0.02250551,0.04445035,0.003705205],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790606,0.0001378069,0.01327348,0.0002383023,0.0001288139,0.001196382,0.0002441825,0.00001028779,0.00571014],"genre_scores_gemma":[0.9813906,0.000003760537,0.01750833,0.0002507339,0.00006474659,0.0000597567,0.0005440523,0.0000116572,0.0001663534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1017021,"threshold_uncertainty_score":0.4944329,"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."}}