Development of a susceptibility gene based novel predictive model for the diagnosis of ulcerative colitis using random forest and artificial neural network.

ABSTRACT: Ulcerative colitis is a type of inflammatory bowel disease characterized by chronic and recurrent nonspecific inflammation of the intestinal tract. To find susceptibility genes and develop a novel predictive model of ulcerative colitis, two sets of cases and a control group containing the ulcerative colitis gene expression profile (training set GSE109142 and validation set GSE92415) were downloaded and used to identify differentially expressed genes. A total of 781 upregulated and 127 downregulated differentially expressed genes were identified in GSE109142. The random forest algorithm was introduced to determine 1 downregulated and 29 upregulated differentially expressed genes contributing highest to ulcerative colitis occurrence. Expression data of these 30 genes were transformed into ge

SUBMITTER: Li H 

PROVIDER: S-EPMC7655162 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature