{"id":"W2973409925","doi":"10.3390/metabo9100200","title":"The metaRbolomics Toolbox in Bioconductor and beyond","year":2019,"lang":"en","type":"review","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Medical Research Council; Alberta Water Research Institute; National Institutes of Health; Agence Nationale de la Recherche; Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen; Horizon 2020 Framework Programme; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Fonds National de la Recherche Luxembourg; World Health Organization; European Commission; Bundesministerium für Bildung und Forschung","keywords":"Workflow; Toolbox; Computer science; Bioconductor; Metabolomics; Scripting language; Data science; Identification (biology); Software; Data mining; Bioinformatics; Database; Chemistry; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006309239,0.0004740264,0.00143918,0.0001546327,0.0001151399,0.0001040151,0.0004113978,0.0003010606,0.000009589622],"category_scores_gemma":[0.0003185342,0.0002876992,0.0003506255,0.0002667521,0.0001674057,0.000004302226,0.0003226883,0.0002720544,0.00002295865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001384937,"about_ca_system_score_gemma":0.0001356064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008801531,"about_ca_topic_score_gemma":0.00002590516,"domain_scores_codex":[0.9980175,0.0002101508,0.0005592158,0.0006408812,0.000127617,0.0004446293],"domain_scores_gemma":[0.9988021,0.0001420763,0.0002560031,0.0006891019,0.00004558165,0.00006515124],"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":[0.00001970234,0.00005815198,0.000111123,0.002551912,0.001206582,0.000003558324,0.00002822026,2.907114e-7,0.001742561,0.01393145,0.00149614,0.9788503],"study_design_scores_gemma":[0.000177248,0.00004172017,0.0000364933,0.00008996735,0.0004852875,0.000008713586,0.00002815146,8.704412e-7,0.0002466637,0.0001477608,0.9984011,0.000336009],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001071912,0.9963324,0.000002980333,0.00004491325,0.000595639,0.0006484907,0.0001046912,0.000006958007,0.001192036],"genre_scores_gemma":[0.00005248793,0.9965532,0.0004023288,0.0000819448,0.0003035505,0.0001223463,0.0001309592,0.00005483487,0.002298276],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.996905,"threshold_uncertainty_score":0.9999575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235704521039688,"score_gpt":0.3096084466187805,"score_spread":0.2772514014083836,"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."}}