{"id":"W4389004796","doi":"10.1093/genetics/iyad204","title":"rvTWAS: identifying gene–trait association using sequences by utilizing transcriptome-directed feature selection","year":2023,"lang":"en","type":"article","venue":"Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Biology; Computational biology; Transcriptome; Feature selection; Genetics; Genetic association; Genome-wide association study; Gene; Feature (linguistics); Computer science; Artificial intelligence; Gene expression; Single-nucleotide polymorphism; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003071275,0.0009919717,0.00106553,0.00154388,0.000515059,0.000851954,0.001246012,0.0005984069,0.001384752],"category_scores_gemma":[0.005501311,0.0004203608,0.002079533,0.001166227,0.0005305631,0.0007748677,0.001325321,0.001234528,0.0006599302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004373553,"about_ca_system_score_gemma":0.001271205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003230769,"about_ca_topic_score_gemma":0.004198793,"domain_scores_codex":[0.9985434,0.0006230274,0.0000901021,0.0004278955,0.0002338348,0.00008180108],"domain_scores_gemma":[0.998312,0.001019703,0.0001640663,0.0002265017,0.0001982593,0.00007938661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007578087,0.0005566461,0.04307968,0.0003698895,0.001485752,0.0009075495,0.0003933654,0.3151564,0.06872821,0.02245297,0.00695554,0.5391562],"study_design_scores_gemma":[0.00003455711,0.0001111159,0.002339832,0.000009250931,0.00006996217,0.0001371335,0.00002461385,0.9791935,0.004587785,0.01188136,0.001587197,0.00002363396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03885049,0.0001453014,0.9576466,0.0001558595,0.00003305944,0.00007297078,0.0005006972,0.002288924,0.0003061469],"genre_scores_gemma":[0.4458522,0.0002395,0.5471929,0.0002894157,0.00008165972,0.0003984182,0.003853329,0.0004692918,0.001623267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003230769,"threshold_uncertainty_score":0.01624268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265521966586779,"score_gpt":0.3022762306993573,"score_spread":0.2696210110334895,"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."}}