{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000544314,0.0001001731,0.0002145368,0.00010874,0.0001052093,0.00001855261,0.0001844735,0.0001529705,0.000001480396],"category_scores_gemma":[0.001456902,0.00007853362,0.00009593186,0.0000451987,0.0001530963,0.00001051166,0.00006719623,0.0002098067,3.767521e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078461,"about_ca_system_score_gemma":0.0006716546,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004437852,"about_ca_topic_score_gemma":0.02263365,"domain_scores_codex":[0.9989612,0.0001550437,0.0003984375,0.0001048213,0.0001542952,0.0002261777],"domain_scores_gemma":[0.9987307,0.00007821414,0.0006743007,0.0000992662,0.0002902608,0.0001272342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009998487,0.00007471934,0.9791974,0.0006402985,0.0004008255,0.000001586267,0.0001568295,0.001554392,0.01560445,0.00005819392,0.000947135,0.0003642648],"study_design_scores_gemma":[0.006324577,0.002969083,0.9002973,0.0005783626,0.0002776574,0.000004111872,0.0004251098,0.004961114,0.08032373,0.0009937905,0.002624252,0.0002208774],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920897,0.0001552645,0.001700797,0.005377056,0.00008531971,0.0004451243,0.0001395836,8.123319e-7,0.000006368202],"genre_scores_gemma":[0.9974368,0.0002132762,0.00174084,0.0004272448,0.00005350017,0.00001989425,0.00003131509,0.000007010403,0.00007008025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07890011,"threshold_uncertainty_score":0.9952008,"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."}}