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author:

Zhou, Xiangdong (Zhou, Xiangdong.) [1] (Scholars:周向东) | Chan, Keith C. C. (Chan, Keith C. C..) [2]

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Abstract:

Multifactor dimensionality reduction (MDR) is originally proposed to identify gene-gene and gene-environment interactions associated with binary traits. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these methods are still not computationally efficient or effective. In this paper, we propose Fuzzy Quantitative trait based Ordinal MDR (QOMDR) to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then using a fuzzy balance accuracy measure based on generalized member function of fuzzy sets to select best sets of SNPs as having strong association with the trait. Experimental results on two real datasets show that our algorithm has better consistency and classification accuracy in identifying gene-gene interactions associated with QTs. © 2016 IEEE.

Keyword:

Artificial intelligence Association reactions Bioinformatics Classification (of information) Genes

Community:

  • [ 1 ] [Zhou, Xiangdong]Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, Hong Kong
  • [ 2 ] [Zhou, Xiangdong]College of Mathematics and Computer Science, Fuzhou University, Fujian, China
  • [ 3 ] [Chan, Keith C. C.]Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, Hong Kong

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Year: 2016

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 1

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