{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01283111,0.001048604,0.001787614,0.006551388,0.001428071,0.008841988,0.002644657,0.003684198,0.002317457],"category_scores_gemma":[0.01341248,0.001105528,0.003327774,0.006491261,0.004169179,0.01677823,0.005124772,0.005126009,0.001433153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001905045,"about_ca_system_score_gemma":0.004017047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003850713,"about_ca_topic_score_gemma":0.004497609,"domain_scores_codex":[0.9935294,0.002965172,0.0006756321,0.0006044985,0.001965106,0.0002600822],"domain_scores_gemma":[0.992889,0.004573585,0.0003294061,0.0009894137,0.001022419,0.0001961596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001695011,0.0002549998,0.001668709,0.001509245,0.0004471722,0.001127548,0.001416033,0.02110602,0.004213455,0.6719092,0.01845845,0.2777196],"study_design_scores_gemma":[0.0000368506,0.0000183361,0.0002818403,0.0003448876,0.000100737,0.000311999,0.0004549516,0.0544281,0.00230235,0.8522679,0.08940114,0.00005092051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004159763,0.005832747,0.9700724,0.01034088,0.000257761,0.0001402513,0.0006181616,0.001217549,0.00736057],"genre_scores_gemma":[0.04278127,0.01077856,0.9385916,0.002526574,0.0003437069,0.0002301542,0.002429597,0.0001529215,0.002165565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01283111,"threshold_uncertainty_score":0.06785822,"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."}}