IEEE Computational Intelligence Magazine - August 2018 - 12
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Identifying DNA Methylation
Modules Associated with
a Cancer by Probabilistic
Evolutionary Learning
Je-Keun Rhee
Cancer Research Institute, College of Medicine,
Catholic University of Korea, Seoul, KoRea
Soo-Jin Kim
Research Institute of agriculture and Life Sciences,
College of agriculture and Life Sciences,
Seoul National University, Seoul, KoRea
Byoung-Tak Zhang
School of Computer Science & engineering,
Seoul National University, Seoul, KoRea
12
IEEE ComputatIonal IntEllIgEnCE magazInE | auguSt 2018
Abstract-DNA methylation leads to inhibition of downstream
gene expression. Recently, considerable studies have been made
to determine the effects of DNA methylation on complex disease. However, further studies are necessary to find the multiple
interactions of many DNA methylation sites and their association with cancer. Here, to assess DNA methylation modules
potentially relevant to disease, we use an Estimation of Distribution Algorithm (EDA) to identify high-order interaction of
DNA methylated sites (or modules) that are potentially relevant
to disease. The method builds a probabilistic dependency
model to produce a solution that is a set of discriminative
methylation sites. The algorithm is applied to array- and
sequencing-based high-throughput DNA methylation profiling
datasets. The experimental results show that it is able to identify
DNA methylation modules for cancer.
1556-603x/18©2018IEEE
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Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2018
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