Majority voting approach for the identification of differentially expressed genes to understand gender-related skeletal muscle aging

Dreder, Abdouladeem, Tahir, Muhammad, Seker, Huseyin and Anwar, Naveed (2016) Majority voting approach for the identification of differentially expressed genes to understand gender-related skeletal muscle aging. In: Computer Science & Information Technology. AIRCC, pp. 237-244. ISBN 978-1-921987-51-9

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Official URL: http://dx.doi.org/10.5121/csit.2016.60619

Abstract

Understanding gene function (GF) is still a significant challenge in system biology. Previously, several machine learning and computational techniques have been used to understand GF. However, these previous attempts have not produced a comprehensive interpretation of the relationship between genes and differences in both age and gender. Although there are several thousand of genes, very few differentially expressed genes play an active role in understanding the age and gender differences. The core aim of this study is to uncover new biomarkers that can contribute towards distinguishing between male and female according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system (MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and ROC). Later, important genes are acquired using majority voting based on the principle that combining multiple models can improve the generalization of the system. Experiments were conducted on Micro Array gene expression dataset and results have indicated a significant increase in classification accuracy when compared with existing system.

Item Type: Book Section
Uncontrolled Keywords: Multi-Filter System, Filter Techniques, Micro Array Gene Expression, Skeletal muscle
Subjects: G700 Artificial Intelligence
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
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Depositing User: Naveed Anwar
Date Deposited: 19 May 2016 13:05
Last Modified: 19 May 2016 13:05
URI: http://nrl.northumbria.ac.uk/id/eprint/26873

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