{"id":"W4391873324","doi":"10.1093/jcag/gwad061.250","title":"A250 CYTOKINE MULTI-OMICS AND MACHINE LEARNING IDENTIFY MIP1ALPHA AS A NOVEL MEDIATOR IN INFLAMMATORY BOWEL DISEASE","year":2024,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Canadian Institutes of Health Research","keywords":"Mediator; Inflammatory bowel disease; Cytokine; Disease; Omics; Medicine; Inflammatory mediator; Inflammation; Immunology; Bioinformatics; Computational biology; Biology; Internal medicine","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":[0.001855322,0.0009782317,0.001420166,0.001568087,0.0003278902,0.001932643,0.0003927711,0.0006518671,0.001702616],"category_scores_gemma":[0.001757482,0.0003003101,0.001397375,0.001297465,0.0003517369,0.0006240933,0.000680822,0.0009461305,0.0006389447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004904065,"about_ca_system_score_gemma":0.0006647402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007580106,"about_ca_topic_score_gemma":0.0007202908,"domain_scores_codex":[0.999557,0.0001594847,0.00003658719,0.0001154982,0.00008354907,0.00004791624],"domain_scores_gemma":[0.9992746,0.0003231855,0.0001858864,0.00006320775,0.00008760141,0.00006573255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004231888,0.0007733936,0.4086994,0.002807842,0.002253754,0.0009154161,0.0002990301,0.07025682,0.2181017,0.003071386,0.006288101,0.2823012],"study_design_scores_gemma":[0.0001797422,0.001181963,0.3026321,0.0006579236,0.001463102,0.001313009,0.0003318407,0.6139379,0.04373759,0.01644657,0.01798396,0.0001342444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.782679,0.03593799,0.1618946,0.004141943,0.0002750414,0.0003331968,0.00949749,0.001771945,0.00346881],"genre_scores_gemma":[0.8954344,0.005997057,0.09185651,0.0005052191,0.0002180173,0.0001885604,0.004822451,0.00009607029,0.0008817381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001932643,"threshold_uncertainty_score":0.009812057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006363546717800468,"score_gpt":0.2302081985502279,"score_spread":0.2238446518324275,"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."}}