{"id":"W2998350645","doi":"10.1101/2019.12.24.870295","title":"Cross-validation of technologies for genotyping <i>CYP2D6</i> and <i>CYP2C19</i>","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Calgary; University of Alberta","funders":"Medical Research Council; Alberta Innovates; European Commission; National Institute for Health and Care Research; King's College London; University of Alberta; Department of Health and Social Care; South London and Maudsley NHS Foundation Trust; GlaxoSmithKline","keywords":"Genotyping; CYP2C19; Pseudogene; TaqMan; Concordance; Haplotype; Genetics; Amplicon; Biology; Pharmacogenomics; Gene; Copy-number variation; Computational biology; CYP2D6; Genotype; Polymerase chain reaction; Genome","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":[0.02365254,0.001454073,0.0009869152,0.002651275,0.0009682449,0.001329958,0.001556225,0.002394091,0.002015754],"category_scores_gemma":[0.02612429,0.001176641,0.001813121,0.001608619,0.001616297,0.0005289612,0.0019885,0.001161551,0.001949587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006711937,"about_ca_system_score_gemma":0.0006186604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001498158,"about_ca_topic_score_gemma":0.002183669,"domain_scores_codex":[0.9388366,0.02945235,0.004471868,0.01541957,0.01014011,0.00167954],"domain_scores_gemma":[0.9705911,0.01276816,0.00340372,0.006128183,0.006324079,0.0007847302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005189852,0.002579346,0.2867664,0.001009383,0.002640509,0.0003254326,0.002838859,0.00576097,0.5951461,0.001705703,0.002077651,0.09395985],"study_design_scores_gemma":[0.0007455329,0.01230786,0.4030261,0.0003372236,0.002486575,0.002797966,0.0005071023,0.01886926,0.5406746,0.001098199,0.01686094,0.0002887143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.761677,0.002125743,0.2216894,0.0002508664,0.0004885615,0.003332649,0.003482699,0.0009573328,0.005995765],"genre_scores_gemma":[0.8026087,0.0006930699,0.1813053,0.0008044411,0.0001534354,0.003586824,0.0083414,0.0003172868,0.002189548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02365254,"threshold_uncertainty_score":0.1250881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05516133409182161,"score_gpt":0.3590568890323318,"score_spread":0.3038955549405102,"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."}}