{"id":"W3100732120","doi":"10.1093/bib/bbaa270","title":"kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes","year":2020,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Calgary","funders":"","keywords":"Kernel (algebra); Computer science; Feature selection; Kernel method; Genetic association; Artificial intelligence; Feature (linguistics); Data mining; Computational biology; Machine learning; Biology; Genotype; Support vector machine; Gene; Genetics; Mathematics; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004454721,0.0001877414,0.000327815,0.0000356335,0.00008306425,0.00003518732,0.00009900363,0.0001613905,0.000003607714],"category_scores_gemma":[0.003073483,0.0001525408,0.00003611145,0.0001129668,0.00008021151,0.00001391326,0.00007668354,0.0001523691,0.000002547669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004272406,"about_ca_system_score_gemma":0.00004702518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004777856,"about_ca_topic_score_gemma":0.00006744816,"domain_scores_codex":[0.9987814,0.00004290841,0.0005333273,0.000210018,0.00013813,0.0002942027],"domain_scores_gemma":[0.9991446,0.0002632177,0.0002799532,0.0001036071,0.0001229067,0.00008578256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003505064,0.0002096144,0.8250757,0.0007896561,0.001087528,0.000007420276,0.01532786,0.0005625601,0.100561,0.00196079,0.03815633,0.01591101],"study_design_scores_gemma":[0.01297485,0.005583995,0.7746071,0.0005086305,0.0005029787,0.00008916068,0.02570181,0.07602017,0.009413566,0.001904053,0.08933281,0.003360944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.860101,0.002117384,0.1258375,0.0107035,0.00008788717,0.000407272,0.0001718128,0.00003369344,0.0005399025],"genre_scores_gemma":[0.8730014,0.0007956236,0.1139388,0.01194533,0.00005291753,0.00002416423,0.0001216425,0.00002170208,0.00009841408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09114746,"threshold_uncertainty_score":0.6220432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01526204203882638,"score_gpt":0.2612022874180093,"score_spread":0.2459402453791829,"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."}}