{"id":"W3135512879","doi":"10.1158/1557-3265.adi21-po-066","title":"Abstract PO-066: Data standardization, integration and meta-analysis of preclinical pharmacogenomics studies for gene expression biomarker discovery","year":2021,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Pharmacogenomics; Precision medicine; Biomarker discovery; Medicine; Biomarker; Computational biology; Drug; Gene expression profiling; Meta-analysis; Bioinformatics; Drug response; Drug discovery; Oncology; Biology; Gene expression; Gene; Pharmacology; Internal medicine; Proteomics; Genetics; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1332908,0.002166351,0.007281835,0.009029657,0.0009475197,0.005944441,0.004689419,0.002204031,0.008101471],"category_scores_gemma":[0.2622281,0.001687938,0.02341381,0.01511,0.00147811,0.002799994,0.004745443,0.003527543,0.00111433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001202215,"about_ca_system_score_gemma":0.004558086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002977141,"about_ca_topic_score_gemma":0.003170191,"domain_scores_codex":[0.8083302,0.1481876,0.01524769,0.01720612,0.00998024,0.001048011],"domain_scores_gemma":[0.7069284,0.2275901,0.0122672,0.04593384,0.005756858,0.001523633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.006995,0.000158521,0.09389307,0.04212157,0.7163113,0.0006086996,0.0003749313,0.02413716,0.004038861,0.005573057,0.02687265,0.07891513],"study_design_scores_gemma":[0.008210118,0.002670738,0.1513051,0.008830884,0.5820431,0.001525983,0.0006099129,0.07591323,0.008990029,0.0431521,0.116135,0.0006138035],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08141612,0.08233423,0.6454009,0.008564021,0.002331611,0.004498625,0.1601834,0.01098816,0.004283031],"genre_scores_gemma":[0.651351,0.008940971,0.2638224,0.002212034,0.0007477456,0.01016752,0.05848467,0.00308026,0.001193453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1332908,"threshold_uncertainty_score":0.7049176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5378205243746687,"score_gpt":0.5835645967090327,"score_spread":0.04574407233436406,"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."}}