{"id":"W2922805334","doi":"10.3390/metabo9030057","title":"MetaboAnalystR 2.0: From Raw Spectra to Biological Insights","year":2019,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":339,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Génome Québec; Genome Canada","keywords":"Workflow; Metabolomics; Computer science; Benchmark (surveying); Raw data; Annotation; Computational biology; Data mining; Bioinformatics; Artificial intelligence; Database; Biology","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":[0.005112067,0.003987172,0.00233762,0.004365455,0.0008658651,0.004532225,0.003653558,0.00132127,0.01667816],"category_scores_gemma":[0.01094613,0.00226809,0.002984679,0.002460931,0.0008565787,0.002092337,0.003841091,0.00235328,0.01277624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009106424,"about_ca_system_score_gemma":0.004007199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004039804,"about_ca_topic_score_gemma":0.005463237,"domain_scores_codex":[0.9983973,0.0002558338,0.000217363,0.0005452404,0.0004693107,0.0001150464],"domain_scores_gemma":[0.99698,0.00114769,0.0004265498,0.0006783336,0.0005870191,0.0001803907],"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":[0.004105727,0.0002867235,0.01643337,0.007845052,0.003281154,0.00261319,0.001880144,0.01502483,0.09797903,0.01437586,0.5888799,0.2472949],"study_design_scores_gemma":[0.001243916,0.0004938099,0.01795656,0.001015531,0.0009279333,0.003027067,0.0004515311,0.1598267,0.1832489,0.03530331,0.5955704,0.0009342894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.009509678,0.001505867,0.2723955,0.0005731592,0.0004061502,0.0004348455,0.06813972,0.6444827,0.0025524],"genre_scores_gemma":[0.05154366,0.001956948,0.6247103,0.001147388,0.0002204717,0.002011974,0.191501,0.1221586,0.004749685],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.01667816,"threshold_uncertainty_score":0.055794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069553652310856,"score_gpt":0.2359653102767074,"score_spread":0.2252697737535988,"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."}}