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Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
Published on: February 24, 2015
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Research Progress in Predicting DNA Methylation Modifications and the Relation with Human Diseases
Chunyan Ao1,2, Lin Gao3, Liang Yu3
1School of Computer Science and Technology, Xidian University, Xi'an, China.
Current Medicinal Chemistry
|September 17, 2021
Summary
This review covers DNA methylation databases, prediction tools, and machine learning for epigenetic regulation. Understanding DNA methylation aids disease diagnosis and treatment.
Area of Science:
- Epigenetics and Molecular Biology
- Genomics and Bioinformatics
Background:
- DNA methylation is a key epigenetic mechanism regulating gene expression.
- It influences vital cellular processes including growth, differentiation, and transformation.
- Dysregulation of DNA methylation is implicated in various diseases.
Purpose of the Study:
- To systematically review current DNA methylation databases and prediction tools.
- To explore machine learning algorithms for predicting DNA methylation modification.
- To summarize the association between DNA methylation and diseases like cancer and Alzheimer's.
Main Methods:
- Literature review of DNA methylation databases and prediction tools.
- Analysis of machine learning approaches for methylation prediction.
- Synthesis of research linking DNA methylation to specific diseases.
Main Results:
- Identification of key DNA methylation databases and prediction tools.
- Overview of machine learning algorithms applied to DNA methylation prediction.
- Compilation of evidence for DNA methylation's role in hypertension, Alzheimer's, diabetic nephropathy, and cancer.
Conclusions:
- Comprehensive understanding of DNA methylation mechanisms is crucial.
- Accurate prediction of DNA methylation modifications can improve disease diagnosis and treatment.
- This review provides a valuable resource for researchers in epigenetics and related fields.
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