An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy

Xiangdong Zhou, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationCIBCB 2016 - Annual IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
PublisherIEEE
ISBN (Electronic)9781467394727
DOIs
Publication statusPublished - 28 Nov 2016
Event13th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2016 - Chiang Mai, Thailand
Duration: 5 Oct 20167 Oct 2016

Conference

Conference13th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2016
Country/TerritoryThailand
CityChiang Mai
Period5/10/167/10/16

Keywords

  • fuzzy accuracy
  • gene-gene interactions
  • multifactor dimensionality reduction
  • ordinal traits
  • quantitative traits

ASJC Scopus subject areas

  • Computational Mathematics
  • Health Informatics
  • Agricultural and Biological Sciences (miscellaneous)
  • Biotechnology
  • Genetics
  • Artificial Intelligence

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