Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74907
Title: A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes
Authors: Zhou, X 
Chan, KCC 
Zhu, D
Keywords: Gene-gene interactions
Multifactor dimensionality reduction
Multiple correlated phenotypes
Ordinal traits
Quantitative traits
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017, 2017, 8058563 How to cite?
Abstract: Multiple correlated phenotypes often appear in complex traits or complex diseases. These correlated phenotypes are useful in identifying gene-gene interactions associated with complex traits or complex disease more effectively. Some approaches have been proposed to use correlation among multiple phenotypes to identify gene-gene interactions that are common to multiple phenotypes. However these approaches either didn't find truly gene-gene interactions or got results which are hard to explain, especially by using all correlated phenotypes to identify gene-gene interactions, they made identified interactions unreliable. In this paper, we propose Multivariate Quantitative trait based Ordinal MDR (MQOMDR) algorithm to effectively identify gene-gene interactions associated with multiple correlated phenotypes by selecting the best classifier according to not only the training accuracy of the phenotype under consideration but also other phenotypes with weights determined mainly by their pair correlation with the phenotype under consideration and also by repeated selection process to make use of truly useful correlated phenotypes. Experimental results on two real datasets show that our algorithm has better performance in identifying gene-gene interactions associated with multiple correlated phenotypes.
Description: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017, Manchester, UK, 23-25 August, 2017
URI: http://hdl.handle.net/10397/74907
ISBN: 9781467389884
DOI: 10.1109/CIBCB.2017.8058563
Appears in Collections:Conference Paper

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