Back to Search
Start Over
Identifying DNA Methylation Modules Associated with a Cancer by Probabilistic Evolutionary Learning
- Source :
- IEEE Computational Intelligence Magazine. 13:12-19
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 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.
- Subjects :
- 0301 basic medicine
Probabilistic logic
Cancer
Methylation
Computational biology
medicine.disease
Theoretical Computer Science
03 medical and health sciences
chemistry.chemical_compound
030104 developmental biology
Discriminative model
Estimation of distribution algorithm
chemistry
Artificial Intelligence
Gene expression
DNA methylation
medicine
DNA
Subjects
Details
- ISSN :
- 15566048 and 1556603X
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- IEEE Computational Intelligence Magazine
- Accession number :
- edsair.doi...........20b8d57a0ed45f438d686853a4fdb57c