{"id":"W2144631782","doi":"10.1093/bib/bbn056","title":"Towards pharmacogenomics knowledge discovery with the semantic web","year":2009,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Pharmacogenomics; Computer science; Semantic Web; Semantics (computer science); Knowledge extraction; Data science; Open Biomedical Ontologies; XML; World Wide Web; Social Semantic Web; Bioinformatics; Artificial intelligence; Biology; OWL-S","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":[0.0002400663,0.000158783,0.0001485123,0.00004362574,0.00008307367,0.00007071465,0.0003068857,0.0001204296,0.000002740207],"category_scores_gemma":[0.00006236975,0.00009678771,0.0000520935,0.0001656994,0.0001923639,0.00001037813,0.00008326286,0.0001523483,0.00001148754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001694576,"about_ca_system_score_gemma":0.0001481841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001137768,"about_ca_topic_score_gemma":0.0000366196,"domain_scores_codex":[0.9991964,0.00002063564,0.0002414805,0.0001366985,0.000117806,0.0002870407],"domain_scores_gemma":[0.9995579,0.00001959093,0.0000905728,0.0002496999,0.00003301306,0.00004924677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004723267,0.0004653611,0.0014273,0.0002950672,0.0001769655,0.00002741369,0.004495741,0.0002203483,0.03828313,0.00130888,0.1012978,0.8515297],"study_design_scores_gemma":[0.002614527,0.001042067,0.007399525,0.0001406483,0.00006255646,0.000132644,0.001243104,0.008890334,0.02455376,0.0002183986,0.9529669,0.0007355689],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9600173,0.00313479,0.01343118,0.008263801,0.0001766771,0.0003640531,0.00002544489,0.00006521142,0.01452151],"genre_scores_gemma":[0.9864904,0.000765298,0.007000729,0.005051384,0.0001302648,0.000009129438,0.00003721992,0.00001251605,0.0005030296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.851669,"threshold_uncertainty_score":0.3946888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01107500414481564,"score_gpt":0.2613136465209089,"score_spread":0.2502386423760932,"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."}}