{"id":"W4383070034","doi":"10.3389/fonc.2023.1199741","title":"Computational pharmacogenotype extraction from clinical next-generation sequencing","year":2023,"lang":"en","type":"article","venue":"Frontiers in Oncology","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Genotyping; Pharmacogenomics; DNA sequencing; 1000 Genomes Project; Reference genome; DPYD; Genetics; Exome sequencing; Whole genome sequencing; Biology; Computational biology; Genotype; Genome; Single-nucleotide polymorphism; Pharmacogenetics; Gene; Mutation","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":[0.001819496,0.001024202,0.001118444,0.001821008,0.0004158238,0.001506982,0.001195829,0.000926843,0.002970021],"category_scores_gemma":[0.00932915,0.0006964304,0.00193998,0.001193984,0.0003765099,0.0005280318,0.001117409,0.001031365,0.000918246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006005129,"about_ca_system_score_gemma":0.002086664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006215874,"about_ca_topic_score_gemma":0.00734712,"domain_scores_codex":[0.9993278,0.0002475582,0.00006421684,0.0001858332,0.000131916,0.0000427338],"domain_scores_gemma":[0.996847,0.002468323,0.0001459247,0.0002717762,0.0002156781,0.00005124628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009332322,0.000163313,0.03689057,0.0007291903,0.0009537737,0.001440154,0.0002031664,0.744954,0.006606115,0.006499382,0.01988537,0.1807417],"study_design_scores_gemma":[0.0001215922,0.00005236072,0.00282186,0.00004115064,0.0001556125,0.0003612483,0.000040374,0.974512,0.002551825,0.01294204,0.006374333,0.00002557051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1415667,0.001309734,0.8141651,0.001757972,0.0001629785,0.0004991781,0.02379983,0.01258276,0.004155708],"genre_scores_gemma":[0.3997609,0.0006248797,0.5550108,0.001272217,0.0001357824,0.0007654901,0.03965555,0.0009802633,0.001794195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006215874,"threshold_uncertainty_score":0.01235938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4059619616281072,"score_gpt":0.5253191977642967,"score_spread":0.1193572361361895,"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."}}