{"id":"W4283014146","doi":"10.21203/rs.3.pex-1920/v1","title":"Using MetaboAnalyst 5.0 Part III: Multi-omics integration","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Génome Québec; Genome Canada","keywords":"Metabolomics; Computer science; Data science; Protocol (science); Bioinformatics; Biology; Medicine","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":[0.005008381,0.004500296,0.002894198,0.004080304,0.00094809,0.005518041,0.001913867,0.001489158,0.02396283],"category_scores_gemma":[0.009909865,0.002081476,0.002980822,0.003859954,0.0005561559,0.00215538,0.002367717,0.002401628,0.01559665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007054714,"about_ca_system_score_gemma":0.002957595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00396224,"about_ca_topic_score_gemma":0.003338227,"domain_scores_codex":[0.9983753,0.0003227014,0.0002191677,0.0004737919,0.0004729249,0.0001359533],"domain_scores_gemma":[0.9976203,0.0009440158,0.0002232781,0.0006721402,0.0004371352,0.0001032091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004422096,0.0005044399,0.01833863,0.01070303,0.006776846,0.002291281,0.00230806,0.01990216,0.2737548,0.02509164,0.3332881,0.3026188],"study_design_scores_gemma":[0.000683472,0.0004902164,0.02415002,0.00097293,0.001900681,0.001561513,0.000497512,0.1162401,0.2710342,0.03970351,0.5420758,0.0006902032],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01013757,0.00136904,0.4683377,0.0005741776,0.0006830069,0.0004386091,0.0847626,0.4295416,0.004155634],"genre_scores_gemma":[0.0335231,0.001275887,0.7896061,0.0004919331,0.0001858837,0.001714096,0.1135289,0.05502984,0.004644227],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02396283,"threshold_uncertainty_score":0.08016366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1619092184766064,"score_gpt":0.4398316238436336,"score_spread":0.2779224053670272,"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."}}