title: Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach.
abstract: Identification of disease genes is a hot topic in biomedicine and genomics. However, it is a challenging problem because of the complexity of diseases. Inflammatory bowel disease (IBD) is an idiopathic disease caused by a dysregulated immune response to host intestinal microflora. It has been proven to be associated with the development of intestinal malignancies. Although the specific pathological characteristics and genetic background of IBD have been partially revealed, it is still an overdetermined disease and the blueprint of all genetic variants still needs to be improved. In this study, a novel computational method was built to identify genes related to IBD. Samples from two subtypes of IBD (ulcerative colitis and Crohn's disease) and normal samples were employed. By analyzing the gene expression profiles of these samples using minimum redundancy maximum relevance and incremental feature selection, 21 genes were obtained that could effectively distinguish samples from the two subtypes of IBD and the normal samples. Then, the shortest-path approach was used to search for an additional 20 genes in a large network constructed using protein-protein interactions based on the above-mentioned 21 genes. Analyses of the 41 genes obtained indicate that they are closely associated with this disease.
authors.affiliation: Department of Science & Technology, Binzhou Medical University Hospital, Binzhou 256603, Shandong, China.
authors.forename: Fei
authors.identifier: 0000-0002-9681-6367
authors.initials: F
authors.lastname: Yuan
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Yu-Hang
authors.initials: YH
authors.lastname: Zhang
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Xiang-Yin
authors.initials: XY
authors.lastname: Kong
authors.affiliation: School of Life Sciences, Shanghai University, Shanghai 200444, China.
authors.forename: Yu-Dong
authors.identifier: 0000-0001-5664-7979
authors.initials: YD
authors.lastname: Cai
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000235:genetics*; D007414:Intestinal Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D025941:Protein Interaction Mapping; D015398:Signal Transduction; D060388:Support Vector Machine