Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning.

ABSTRACT: Ulcerative colitis (UC) is one of the most common forms of inflammatory bowel disease (IBD) characterized by inflammation of the mucosal layer of the colon. Diagnosis of UC is based on clinical symptoms, and then confirmed based on endoscopic, histologic and laboratory findings. Feature selection and machine learning have been previously used for creating models to facilitate the diagnosis of certain diseases. In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples. We validated our model with an independent gene expression dataset of colonic samples from subjects in active

SUBMITTER: Khorasani HM 

PROVIDER: S-EPMC7426912 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature