{"id":"W4393861045","doi":"10.3390/metabo14040200","title":"Challenges in the Metabolomics-Based Biomarker Validation Pipeline","year":2024,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"University Health Network Foundation; Department of Medicine, University of Toronto; Krembil Foundation; University of Toronto","keywords":"Metabolomics; Biomarker discovery; Biomarker; Computer science; Computational biology; Bioanalysis; Biochemical engineering; Data science; Bioinformatics; Proteomics; Nanotechnology; Biology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00112748,0.0001959396,0.0002193333,0.0001833364,0.00005746103,0.00008668606,0.000257792,0.00009099571,0.00003259259],"category_scores_gemma":[0.0001970968,0.0001285674,0.0001451882,0.0003367075,0.00006374052,0.000006970229,0.00005971368,0.0001106639,0.00002580271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006738577,"about_ca_system_score_gemma":0.00004515095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085062,"about_ca_topic_score_gemma":0.0000367191,"domain_scores_codex":[0.9986714,0.0001887788,0.0002563266,0.0004421497,0.000170079,0.0002713143],"domain_scores_gemma":[0.9993962,0.00007738202,0.0000383395,0.0004106006,0.00004676633,0.00003069599],"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":[0.00009073595,0.0002257546,0.001835924,0.0001406098,0.0002600086,0.00001781298,0.0003160334,0.00005766529,0.8617312,0.04604745,0.006542628,0.0827342],"study_design_scores_gemma":[0.0003346037,0.00004213855,0.01084715,0.00001328434,0.00009102986,0.000008786716,0.0002131356,0.0006097351,0.1740577,0.001419504,0.8121334,0.0002296173],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4537568,0.5238565,0.002359913,0.007275439,0.000885251,0.0004356814,0.00006262707,0.00005867055,0.01130911],"genre_scores_gemma":[0.9818953,0.01586814,0.0009745972,0.0003659847,0.0003459981,0.0000826552,0.0001219725,0.00002486379,0.0003205383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8055907,"threshold_uncertainty_score":0.5242826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05080735331619465,"score_gpt":0.3025779116668548,"score_spread":0.2517705583506602,"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."}}