{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04850637,0.001447545,0.002386541,0.004053254,0.0008828772,0.006593138,0.002310459,0.002355575,0.002523497],"category_scores_gemma":[0.0604817,0.0008168591,0.001114317,0.004064096,0.002729598,0.0112157,0.005197429,0.004105737,0.0009958412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001993245,"about_ca_system_score_gemma":0.004114829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001648541,"about_ca_topic_score_gemma":0.002546833,"domain_scores_codex":[0.9832597,0.008277803,0.001365927,0.001037067,0.005520138,0.0005393864],"domain_scores_gemma":[0.9262441,0.04727006,0.005961836,0.005204344,0.0139683,0.001351423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009980566,0.0002532038,0.02367366,0.004669398,0.0009659973,0.0003798378,0.001176402,0.00882724,0.07559583,0.03493568,0.01920301,0.8293217],"study_design_scores_gemma":[0.0002802701,0.001769747,0.05339889,0.00478933,0.001192319,0.003401191,0.002708897,0.06740955,0.1902456,0.4156998,0.2578743,0.001230026],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06176157,0.1768918,0.6248519,0.1195622,0.002002122,0.0004680337,0.002278621,0.003651354,0.008532392],"genre_scores_gemma":[0.2274211,0.1017648,0.6521078,0.009700567,0.004854055,0.000300094,0.001611075,0.0007588895,0.001481585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04850637,"threshold_uncertainty_score":0.2565292,"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."}}