{"id":"W4318269899","doi":"10.1016/b978-0-323-99924-3.00006-6","title":"Metabolomics for personalized medicine","year":2023,"lang":"en","type":"book-chapter","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Metabolomics; Computational biology; Omics; Personalized medicine; Genomics; Precision medicine; Biomarker discovery; Biomarker; Biology; Phenotype; Pharmacogenomics; Bioinformatics; Epigenetics; Metabolome; Proteomics; Gene; Genetics; Genome","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009879072,0.001144013,0.00102088,0.001297105,0.0005210713,0.003075552,0.001078489,0.002404253,0.03441538],"category_scores_gemma":[0.001114246,0.0003377422,0.000489281,0.00121759,0.001710185,0.00336701,0.002122155,0.0036122,0.02139616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232614,"about_ca_system_score_gemma":0.0009359577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008853298,"about_ca_topic_score_gemma":0.002203191,"domain_scores_codex":[0.9994206,0.0001539926,0.00001801017,0.00009173209,0.0002834632,0.00003226671],"domain_scores_gemma":[0.9996636,0.0001883335,0.00001514023,0.00004658097,0.00005816589,0.0000281868],"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":[0.0000363518,0.00004996061,0.00008425368,0.0006176686,0.00002992117,0.0001015624,0.000159177,0.0004352017,0.002138766,0.2315568,0.3536318,0.4111586],"study_design_scores_gemma":[0.000003444548,0.00001317432,0.0001134613,0.0002049777,0.000005154637,0.0002064756,0.00004331874,0.0002133698,0.0003635981,0.07828823,0.9205358,0.000008920873],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.0007351932,0.4443339,0.0631834,0.04199211,0.01343625,0.0001054763,0.0005750812,0.001021922,0.4346166],"genre_scores_gemma":[0.01025121,0.2286154,0.04892565,0.02176154,0.009768438,0.0002084925,0.0005150504,0.0004683571,0.6794859],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03441538,"threshold_uncertainty_score":0.115131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03510066647178415,"score_gpt":0.2780697156879201,"score_spread":0.242969049216136,"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."}}