{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009636803,0.0002455545,0.0007877306,0.0002608728,0.0002457853,0.0000838016,0.0001672822,0.0001641104,0.00000895106],"category_scores_gemma":[0.0006632279,0.0002079465,0.0003330669,0.0007869353,0.00004206956,0.0000325305,0.0002333603,0.00007836598,0.000006420756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004217301,"about_ca_system_score_gemma":0.00002675199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001007309,"about_ca_topic_score_gemma":0.0001167647,"domain_scores_codex":[0.9984007,0.00007814509,0.0005430589,0.0002969833,0.0002672533,0.0004138865],"domain_scores_gemma":[0.9986523,0.0003100262,0.0004135586,0.0003303755,0.0001974736,0.00009631433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002375443,0.0001247922,0.6967573,0.000351875,0.2190828,0.000002394871,0.002615666,0.02476169,0.007608626,0.0009017774,0.04006832,0.007487206],"study_design_scores_gemma":[0.002952889,0.0008550209,0.7634515,0.00000718316,0.09054742,0.000001088316,0.003277283,0.02011163,0.007031854,0.003922083,0.1062424,0.00159958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769853,0.004637675,0.01582523,0.0007844286,0.0002585365,0.0008608196,0.0004396127,0.00008158815,0.0001268538],"genre_scores_gemma":[0.9440792,0.01009166,0.03907293,0.0008858791,0.0007279716,0.000340071,0.003289456,0.00005311832,0.001459679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1285354,"threshold_uncertainty_score":0.8479813,"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."}}