{"id":"W4407285924","doi":"10.1093/jcag/gwae059.155","title":"A155 STOOL-BASED PROTEIN SIGNATURES FOR NON-INVASIVE ACCURATE DIAGNOSIS AND SUBTYPING OF INFLAMMATORY BOWEL DISEASE THROUGH HIGH-THROUGHPUT PROTEOMICS AND MACHINE LEARNING APPROACHES","year":2025,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Subtyping; Proteomics; Inflammatory bowel disease; Disease; Computational biology; Computer science; Throughput; Artificial intelligence; Machine learning; Medicine; Biology; Pathology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001151953,0.0006518985,0.0006817183,0.001623661,0.0003852489,0.001004354,0.0003728897,0.0005204597,0.0008266132],"category_scores_gemma":[0.001479555,0.0002741291,0.000552644,0.001053072,0.0002508881,0.0006743547,0.0005885538,0.0005853976,0.0005789436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003193348,"about_ca_system_score_gemma":0.0004684111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005447696,"about_ca_topic_score_gemma":0.0008746885,"domain_scores_codex":[0.9994051,0.0001690771,0.0000588655,0.000156131,0.0001687161,0.0000420566],"domain_scores_gemma":[0.9994036,0.0001626047,0.0001560858,0.0000647929,0.0001548116,0.00005816013],"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":[0.001051701,0.000588981,0.0520197,0.0005698135,0.0002556388,0.0003243697,0.00009351349,0.007836351,0.8190535,0.0004729942,0.001197822,0.1165357],"study_design_scores_gemma":[0.0001598976,0.00180645,0.1726223,0.000123022,0.0003967047,0.002432565,0.0002144389,0.330232,0.4832001,0.002402256,0.006292208,0.0001180938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7904333,0.004471167,0.1989196,0.000539555,0.0001263106,0.000291126,0.002335469,0.001388335,0.001495138],"genre_scores_gemma":[0.7988257,0.0008259538,0.1977756,0.0001946824,0.00003992918,0.000140779,0.001381977,0.0000666843,0.000748779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001623661,"threshold_uncertainty_score":0.006092191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367295328901456,"score_gpt":0.2349300229619071,"score_spread":0.2212570696728925,"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."}}