{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008978226,0.0009163615,0.001622937,0.0003302885,0.0002469114,0.00003662252,0.0006444537,0.0008878509,0.0002441357],"category_scores_gemma":[0.000547678,0.000824407,0.0007860566,0.00008352566,0.000531913,0.000004601972,0.0003960527,0.0003753138,0.0001387453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003889745,"about_ca_system_score_gemma":0.0001665244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000750428,"about_ca_topic_score_gemma":0.00003292734,"domain_scores_codex":[0.9967952,0.00003654726,0.0008440627,0.001262127,0.0003761193,0.0006858784],"domain_scores_gemma":[0.9976899,0.0001218049,0.0005723911,0.001017339,0.0003946858,0.0002038971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004097481,0.00002768671,0.00001283862,0.0001349143,0.002296046,0.000006560466,0.00005914609,0.00000968067,0.04353704,0.830526,0.1196114,0.003368917],"study_design_scores_gemma":[0.001721546,0.0003221959,0.00002165412,0.00003443488,0.000995806,0.00001221874,0.0000430649,0.00002565576,0.002576909,0.02717162,0.9662496,0.0008252793],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003363812,0.1633851,0.02489633,0.006161627,0.01664433,0.006898858,0.007321514,0.0006859927,0.7706424],"genre_scores_gemma":[0.0002433732,0.04938633,0.005146107,0.000896539,0.003365954,0.0001778365,0.00235483,0.0004329309,0.9379961],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8466382,"threshold_uncertainty_score":0.9994207,"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."}}