{"id":"W2804197647","doi":"10.1039/c8cc02592d","title":"Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery","year":2018,"lang":"en","type":"article","venue":"Chemical Communications","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lipidomics; Metabolome; Metabolomics; Biomarker discovery; Biomarker; Computational biology; Chemistry; Bioinformatics; Biology; Proteomics; Chromatography; Biochemistry","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":[0.0006301404,0.0001720133,0.000288666,0.00004172396,0.0002845721,0.00008729458,0.0007274332,0.0001118415,0.000001314571],"category_scores_gemma":[0.001167079,0.0001627388,0.00004935332,0.00009691172,0.0005541609,0.00003195621,0.002818512,0.0001051676,6.436474e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001126304,"about_ca_system_score_gemma":0.00004794881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002964676,"about_ca_topic_score_gemma":0.00006378707,"domain_scores_codex":[0.9988274,0.00006252451,0.0003058244,0.0004861669,0.00006411007,0.0002539988],"domain_scores_gemma":[0.9974979,0.0001943127,0.0001440718,0.001964116,0.0001122753,0.0000872919],"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":[0.00007005343,0.00004249625,0.0004278839,0.00002701593,0.0001360948,2.846966e-8,0.00002644144,4.612883e-8,0.9854622,0.004743011,0.0006293932,0.0084353],"study_design_scores_gemma":[0.002377063,0.0001558967,0.002055407,0.00001881266,0.0004130288,0.00001874469,0.0003522222,0.002862214,0.4627142,0.003923437,0.5242944,0.0008145818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272451,0.02222856,0.0467265,0.001666858,0.0001653664,0.0004600939,0.0008396067,0.00002627786,0.0006416301],"genre_scores_gemma":[0.9252968,0.0100121,0.06285525,0.0004531958,0.0002260838,0.0000494866,0.0008978437,0.00003005527,0.0001791548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5236651,"threshold_uncertainty_score":0.6636294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05899284843965059,"score_gpt":0.3574879475514485,"score_spread":0.2984950991117979,"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."}}