{"id":"W7133046425","doi":"","title":"Characterizing Genotyping Methods for CYP2A6, Their Impacts on Prediction of Nicotine Metabolism, and Applications for the Discovery of Novel Associations","year":2025,"lang":"","type":"dissertation","venue":"TSpace","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Common Fund; National Human Genome Research Institute; National Institute on Drug Abuse; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; NIH Office of the Director; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Genotyping; Genotype; CYP2A6; 1000 Genomes Project; Amplicon; Genome-wide association study; Imputation (statistics); Nicotine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01567924,0.0009828373,0.0009022309,0.001884988,0.0005096787,0.002001524,0.001224213,0.0015422,0.001101656],"category_scores_gemma":[0.02811033,0.0005816111,0.001166811,0.002133472,0.00081486,0.001024695,0.0009942524,0.001970127,0.00136238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006188736,"about_ca_system_score_gemma":0.0008732999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001404237,"about_ca_topic_score_gemma":0.002633693,"domain_scores_codex":[0.9827935,0.007665313,0.000881635,0.003489635,0.004899584,0.0002704583],"domain_scores_gemma":[0.9835242,0.007978665,0.003053619,0.002780175,0.002440602,0.0002226413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004901863,0.0002511161,0.1573015,0.0009912362,0.001154141,0.0001712955,0.0006229015,0.01401565,0.3082402,0.0131775,0.004745021,0.4988393],"study_design_scores_gemma":[0.0001478147,0.001077695,0.1594702,0.0004624643,0.001059844,0.002382746,0.0002305402,0.1241135,0.6278797,0.02226713,0.06056117,0.0003471407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.101213,0.005414477,0.8843762,0.001381377,0.0002869847,0.000341966,0.002192377,0.001324678,0.00346894],"genre_scores_gemma":[0.2523133,0.003477161,0.7352486,0.0008844443,0.0002773497,0.0005916765,0.002723003,0.0004471229,0.004037431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01567924,"threshold_uncertainty_score":0.08292073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1399656161753509,"score_gpt":0.5006515224826439,"score_spread":0.360685906307293,"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."}}