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

Abdouladeem Dreder, Muhammad Tahir, Huseyin Seker, Naveed Anwar

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.
Original languageEnglish
Title of host publicationComputer Science & Information Technology
EditorsJan Zizka, Dhinaharan Nagamalai
PublisherAIRCC
Pages237-244
Volume53
ISBN (Print)978-1-921987-51-9
Publication statusPublished - May 2016
EventThe Sixth International Conference on Computer Science, Engineering and Information Technology (CCSEIT 2016) - Vienna, Austria
Duration: 1 May 2016 → …
http://airccse.org/V6N53.html

Conference

ConferenceThe Sixth International Conference on Computer Science, Engineering and Information Technology (CCSEIT 2016)
Period1/05/16 → …
Internet address

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