The brief description on the WTCCC dataset.

<div><p>Genome-wide association studies typically considers epistatic interactions as a crucial factor in exploring complex diseases. However, the current methods primarily concentrate on the detection of two-order epistatic interactions, with flaws in accuracy. In this work, we introduc...

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Bibliographic Details
Main Author:	Liyan Sun (760586) (author)
Other Authors:	Jingwen Bian (19942771) (author), Yi Xin (146413) (author), Linqing Jiang (19942774) (author), Linxuan Zheng (19942777) (author)
Published:	2024
Subjects:	Biochemistry Genetics Molecular Biology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm multiple objective functions experimental results demonstrate comparative toxicogenomics database alongside 800 case algorithms &# 8217 800 control samples detecting epistatic interactions ssa draws inspiration ssa algorithm outperforms exploring complex diseases xlink "> genome uncovering numerous genes order epistatic interactions highest average f data set increases seven complex diseases epistasis detection tasks dnme 100 datasets complex diseases xlink "> data increases average f detecting epistasis dnme 100 five datasets dnme 1000 order epistasis epistasis rises dnme3 100 dme 100 wtccc dataset source code six third significant role relatedly reported properties consistent potent tool population based otherwise paralleling might play marginal effects identifying higher higher level gene pairs dme 1000 detect high ctd ). crucial factor comprehensive comparison better utilized aids us 6sqwj /</
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The brief description on the WTCCC dataset.

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