{"id":"W4282559158","doi":"10.3390/metabo12060526","title":"mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights","year":2022,"lang":"en","type":"article","venue":"Metabolites","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"Single-nucleotide polymorphism; Computational biology; Biology; Metabolomics; Gene; SNP; Bioinformatics; Genome; Genetics; 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":[0.0001896173,0.0001901994,0.0002256386,0.00007028014,0.0004373706,0.00006083361,0.0001650895,0.00006696269,0.00005040399],"category_scores_gemma":[0.000030486,0.0001761283,0.0001391442,0.0001044145,0.00006434018,0.000009753669,0.0003147886,0.0001023903,0.000002278043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001004799,"about_ca_system_score_gemma":0.00007248535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004295607,"about_ca_topic_score_gemma":0.000002967907,"domain_scores_codex":[0.9988937,0.00005407561,0.0002724887,0.0003282511,0.000166664,0.0002847953],"domain_scores_gemma":[0.9993975,0.00003690565,0.0001068668,0.0002756582,0.00006863789,0.0001144257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001359895,0.0007735849,0.02783035,0.0004513433,0.001958032,0.000008576926,0.001355425,0.005189866,0.536369,0.1122811,0.0427251,0.2696978],"study_design_scores_gemma":[0.0007769103,0.0001098647,0.003327865,0.00000296791,0.0001265415,0.00001332331,0.000188753,0.001398241,0.00905073,0.004383447,0.980338,0.000283318],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6005896,0.3790437,0.01653594,0.0002776866,0.001380579,0.000772604,0.000826009,0.0000569753,0.0005168865],"genre_scores_gemma":[0.9857056,0.002572122,0.004565102,0.001436676,0.00137453,0.0004527391,0.00292188,0.00005124757,0.0009201784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.937613,"threshold_uncertainty_score":0.7182305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296967275119484,"score_gpt":0.215356540826241,"score_spread":0.2023868680750462,"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."}}