An Alzheimers disease related genes identification method based on multiple classifier integration. (October 2017)

• Record Type: Journal Article
• Authors: Miao, Yu; Jiang, Huiyan; Liu, Huiling; Yao, Yu-dong
• Is Part Of: Computer methods and programs in biomedicine. Volume 150(2017)
• Page Start: 107
• Page End: 115
• Publication Date: 2017-10
• Subjects: Gene identification -- Feature selection -- Cascading classifier -- Majority voting -- Alzheimers disease; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.08.006
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: Background and Objective: Alzheimers disease (AD) is a fatal neurodegenerative disease and the onset of AD is insidious. Full understanding of the AD-related genes (ADGs) has not been completed. The National Center for Biotechnology Information (NCBI) provides an AD dataset of 22, 283 genes. Among these genes, 71 genes have been identified as ADGs. But there may still be underlying ADGs that have not yet been identified in the remaining 22, 212 genes. This paper aims to identify additional ADGs using machine learning techniques. Methods: To improve the accuracy of ADG identification, we propose a gene identification method through multiple classifier integration. First, a feature selection algorithm is applied to select the most relevant attributes. Second, a two-stage cascading classifier is developed to identify ADGs. The first stage classification task is based on the relevance vector machine and, in the second stage, the results of three classifiers, support vector machine, random forest and extreme learning machine, are combined through voting. Results: According to our results, feature selection improves accuracy and reduces training time. Voting based classifier reduces the classification errors. The proposed ADG identification system provides accuracy, sensitivity and specificity at levels of 78.77%, 83.10% and 74.67%, respectively. Based on the proposed ADG identification method, potentially additional ADGs are identified and top 13 genes (predicted ADGs) are presented. Conclusions: In this paper, an ADG identification method for identifying ADGs is presented. The proposed method which combines feature selection, cascading classifier and majority voting leads to higher specificity and significantly increases the accuracy and sensitivity of ADG identification. Potentially new ADGs are identified.