{"id":"W3022658502","doi":"10.3390/metabo10050186","title":"MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics","year":2020,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":588,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Cancer Institute","keywords":"Workflow; Pipeline (software); Computer science; Benchmark (surveying); Metabolomics; Metabolome; Pipeline transport; Data mining; Key (lock); Computational biology; Bioinformatics; Chemistry; Database; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003932127,0.0004128199,0.0008118579,0.00004921278,0.0001541625,0.0001062759,0.000523037,0.0001737771,0.00005754905],"category_scores_gemma":[0.0005934803,0.0003646077,0.0004825082,0.0004208316,0.0001023061,0.00001777016,0.0002098041,0.0001002067,0.00001185668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001147738,"about_ca_system_score_gemma":0.0000862804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001040116,"about_ca_topic_score_gemma":0.00000792846,"domain_scores_codex":[0.997728,0.000129008,0.0004786998,0.0008609571,0.0002200287,0.0005832987],"domain_scores_gemma":[0.9986972,0.00002694395,0.0001805969,0.0004914374,0.0002353932,0.0003684898],"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":[0.002218777,0.0004180882,0.006646957,0.0001291121,0.003800167,0.00000612065,0.0003076488,0.001170477,0.896296,0.05527399,0.007563187,0.02616949],"study_design_scores_gemma":[0.003723874,0.0005987483,0.002626198,0.000003504022,0.001177248,0.000007710213,0.0003201394,0.002029143,0.22484,0.003341703,0.7603642,0.0009674754],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.611482,0.09120719,0.2835587,0.005587293,0.001337422,0.001901634,0.001451147,0.0002487777,0.003225841],"genre_scores_gemma":[0.7416083,0.003194309,0.2484785,0.003517681,0.001923946,0.0002128207,0.0006625659,0.00007734424,0.000324543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7528011,"threshold_uncertainty_score":0.9998806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0301003328465878,"score_gpt":0.2846727995211495,"score_spread":0.2545724666745617,"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."}}