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Computational-Based Approaches in Epigenetic Research: Insights from Computational Tools, Mathematical Models, and Machine Learning Methods

Roghayeh Ghorbani, Reza Shokri-Gharelo.

Abstract
Understudying epigenetics underlying mechanisms is essential. Studies have indicated functional roles of epigenetic events, including DNA methylation and histone modifications, in human disease, stem cell growth and development, aging, response to environmental stresses, and species evolution. High-throughput sequencing techniques alongside routine experimental approaches have rapidly produced a bulk of data that the major part of them remained unprocessed. To analyze, interpret, and process of these data, availability of efficient computational methods is critical. Epigenetic data analysis is complex and difficult because these data contain multi-layer set of information. The methods implemented to this purpose must be able to handle massive data of experimental works and process the epigenetic layers. Furthermore, the methods must be capable of integrating multiple modifications and their combination effects on chromatin conformational structure and consequently the expression network of genes. In this study, we briefly reviewed challenges in the way of the computational epigenetics, the latest reported methods, and significant biological results derived from applying computational-based methods on epigenetic data.

Key words: Bioinformatics; Computational Epigenetics; Data Analysis; DNA Methylation; Epigenomics



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