{"id":"W4366990879","doi":"10.3390/metabo13050589","title":"A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients","year":2023,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Metabolomics; Colorectal cancer; Biomarker; Metabolite; Biomarker discovery; Medicine; Internal medicine; Multivariate analysis; Cancer; Oncology; Bioinformatics; Biology; Biochemistry; Proteomics","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":[0.00051714,0.000324018,0.0004516255,0.0001655935,0.0003383406,0.000120296,0.0001665868,0.0001906794,0.00001250382],"category_scores_gemma":[0.0009364752,0.0002879,0.0002042889,0.0003889962,0.0001291146,0.00001632454,0.0002168967,0.0001747172,0.00000326455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001430548,"about_ca_system_score_gemma":0.00005605275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000458416,"about_ca_topic_score_gemma":0.0000339433,"domain_scores_codex":[0.9981976,0.0001092282,0.0003596019,0.000628943,0.0001572671,0.0005474152],"domain_scores_gemma":[0.9992043,0.0001720567,0.0001966767,0.0001966395,0.0001290874,0.0001012861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002512943,0.00008179502,0.2084105,0.00006509294,0.0003118387,8.434348e-7,0.00005593353,0.00008269989,0.7870297,0.0004926316,0.0003116112,0.002906078],"study_design_scores_gemma":[0.0025538,0.0003659195,0.1726663,0.00004335863,0.0004122425,0.000001628522,0.00009711885,0.00541295,0.7149688,0.0005068962,0.1022309,0.0007400842],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976173,0.01865491,0.003262814,0.000187802,0.0004919977,0.0005175213,0.0006141757,0.00006658133,0.00003112936],"genre_scores_gemma":[0.9911072,0.003086847,0.003670167,0.0001365364,0.000294521,0.0002152545,0.0007443842,0.00007190926,0.0006731658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1019193,"threshold_uncertainty_score":0.9999573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176345256768519,"score_gpt":0.2577421927841015,"score_spread":0.2459787402164164,"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."}}