{"id":"W2520222164","doi":"10.1002/cpbi.11","title":"Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis","year":2016,"lang":"en","type":"review","venue":"Current Protocols in Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1574,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; McGill University; National Institute for Nanotechnology; Ste. Anne's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Metabolomics; Univariate; Computer science; Linear discriminant analysis; Principal component analysis; Data mining; Artificial intelligence; Multivariate statistics; Chemistry; Machine learning","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":[0.003188869,0.002652975,0.003189478,0.006593107,0.0005002457,0.002865546,0.002880065,0.001076128,0.03144386],"category_scores_gemma":[0.00296591,0.00158463,0.002305111,0.004714011,0.0005204275,0.002371251,0.002188704,0.002931419,0.04025662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007776346,"about_ca_system_score_gemma":0.002471114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00179344,"about_ca_topic_score_gemma":0.002309198,"domain_scores_codex":[0.998792,0.0002033986,0.0001664343,0.0002087245,0.0005665795,0.00006280634],"domain_scores_gemma":[0.9983572,0.0004474616,0.0001953065,0.0001995236,0.0006974287,0.0001030587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003534358,0.00007464465,0.0008442579,0.01751428,0.0006536179,0.0005574,0.0001937355,0.002377095,0.04644526,0.01216169,0.3699265,0.5488982],"study_design_scores_gemma":[0.00006048927,0.00005278237,0.001629652,0.0006838336,0.0001482433,0.0008345346,0.00003481188,0.002787211,0.02425515,0.00517856,0.9642075,0.0001272193],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002636111,0.1142719,0.580447,0.002263701,0.001993062,0.001419748,0.07431786,0.1957619,0.02688875],"genre_scores_gemma":[0.01089532,0.08215168,0.746781,0.001590255,0.000620003,0.00320169,0.1088906,0.02596349,0.01990598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03144386,"threshold_uncertainty_score":0.1051902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3626876187542807,"score_gpt":0.4984211420443799,"score_spread":0.1357335232900991,"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."}}