{"id":"W4386084463","doi":"10.1093/bioinformatics/btad523","title":"metGWAS 1.0: an R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome-wide association studies","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto; Canadian Institute for Advanced Research","funders":"National Heart, Lung, and Blood Institute; Banting and Best Diabetes Centre, University of Toronto; Natural Sciences and Engineering Research Council of Canada; Diabetes Canada; Canadian Institutes of Health Research; U.S. Department of Veterans Affairs","keywords":"Metabolomics; Genome-wide association study; Computational biology; Workflow; Genetic association; Biology; Single-nucleotide polymorphism; Genomics; Computer science; Bioinformatics; Genome; Genetics; Gene; Database; 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":[0.008067191,0.004387118,0.002227108,0.003488672,0.001093506,0.003448887,0.003904799,0.001055238,0.04015167],"category_scores_gemma":[0.02163121,0.002229187,0.003881581,0.001969915,0.0009908689,0.00175064,0.004203757,0.003372534,0.0199586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245011,"about_ca_system_score_gemma":0.004961301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00470106,"about_ca_topic_score_gemma":0.006798481,"domain_scores_codex":[0.9976819,0.000799771,0.0002603031,0.000656873,0.0004066869,0.0001944645],"domain_scores_gemma":[0.9922022,0.004849901,0.0006713629,0.001046222,0.0008876817,0.0003426536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004300538,0.0003880945,0.02281434,0.01007458,0.00644386,0.002961479,0.002111308,0.06193211,0.03726322,0.03120127,0.5918652,0.2286439],"study_design_scores_gemma":[0.002277892,0.0004655881,0.01670177,0.001126105,0.001486704,0.001962936,0.00057752,0.3911212,0.04418599,0.1262321,0.4130238,0.0008383624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004673054,0.000463341,0.5355865,0.0005680517,0.0002915502,0.0008868967,0.05781853,0.3977331,0.001979022],"genre_scores_gemma":[0.05599849,0.0006641745,0.7777324,0.0007881357,0.0001209765,0.005314007,0.07694092,0.07855859,0.003882351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04015167,"threshold_uncertainty_score":0.1343208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04919634475375474,"score_gpt":0.3230295530492259,"score_spread":0.2738332082954712,"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."}}