Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset.

Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset.

<p>Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset.</p>

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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
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