{"id":"W2952541172","doi":"10.1038/s41397-018-0048-y","title":"Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes","year":2018,"lang":"en","type":"article","venue":"The Pharmacogenomics Journal","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Pharmacogenomics; Linkage disequilibrium; Genetics; Biology; Genome-wide association study; Genome; Genetic association; 1000 Genomes Project; Gene; Computational biology; Haplotype; Single-nucleotide polymorphism; Allele; Genotype","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005327204,0.0008757467,0.0008631214,0.0007316089,0.00169638,0.0003530479,0.002253096,0.0003545071,0.008944837],"category_scores_gemma":[0.00006765433,0.000691379,0.0005175713,0.0008753652,0.0008556967,0.0005496347,0.0005654936,0.00290179,0.002751677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022339,"about_ca_system_score_gemma":0.0008225396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001412421,"about_ca_topic_score_gemma":0.00004274348,"domain_scores_codex":[0.993318,0.001761119,0.001592523,0.0007879006,0.000549244,0.001991198],"domain_scores_gemma":[0.9967761,0.0004174848,0.0008070352,0.0005727839,0.0004367671,0.0009898086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009834871,0.0002734937,0.001539119,0.00002132251,0.0009289783,0.0003953735,0.00228668,0.009647392,0.9628093,0.0001184997,0.003825396,0.01717094],"study_design_scores_gemma":[0.01314979,0.000304587,0.007054435,0.00004420636,0.001630867,0.003665091,0.0006447953,0.02741025,0.3990183,0.00548501,0.5396796,0.001913068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731891,0.01163107,0.0004245838,0.001543886,0.00848827,0.0008160105,0.0003944128,0.0001031495,0.003409593],"genre_scores_gemma":[0.9753331,0.01067426,0.0002569412,0.004085041,0.008117746,0.00004932345,0.00004336785,0.0001688216,0.001271419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.563791,"threshold_uncertainty_score":0.9996033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09886308670490605,"score_gpt":0.4169834628383943,"score_spread":0.3181203761334882,"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."}}