CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies | Zendy

ChengHong Yang | Zendy; LiYeh Chuang | Zendy; YuDa Lin | Zendy

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CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies

Author(s) -

ChengHong Yang,

LiYeh Chuang,

YuDa Lin

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx163

Subject(s) - epistasis , genome wide association study , multifactor dimensionality reduction , computer science , snp , curse of dimensionality , genetic association , single nucleotide polymorphism , data mining , computational biology , machine learning , biology , genetics , genotype , gene

Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in large-scale SNP datasets.

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