{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1483619,0.002164212,0.004425188,0.005482607,0.002385495,0.01426055,0.006639449,0.005944915,0.002395838],"category_scores_gemma":[0.1011705,0.00230776,0.002319905,0.003440065,0.006515597,0.009157868,0.009957207,0.01330535,0.004124074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004381068,"about_ca_system_score_gemma":0.02257489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003849358,"about_ca_topic_score_gemma":0.003167194,"domain_scores_codex":[0.944046,0.02416707,0.00463614,0.006305363,0.01962362,0.001221703],"domain_scores_gemma":[0.8559117,0.07442498,0.007220914,0.008637137,0.05045461,0.003350515],"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":[0.001193584,0.0004254145,0.01409758,0.01461614,0.001369236,0.00178036,0.002608587,0.0130239,0.07314306,0.07279237,0.0456693,0.7592804],"study_design_scores_gemma":[0.0002322969,0.001837896,0.01235451,0.008050366,0.000949021,0.004846684,0.002827185,0.0395917,0.06773274,0.2318541,0.6289824,0.0007410685],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01753389,0.1592267,0.6909798,0.1111322,0.004227491,0.002273695,0.002397445,0.003949294,0.008279569],"genre_scores_gemma":[0.09007058,0.1085461,0.7426934,0.04096877,0.004642971,0.003184168,0.004167407,0.001246759,0.004479879],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1483619,"threshold_uncertainty_score":0.7846221,"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."}}