Author(s)

Si Chen

Corresponding Author

Si Chen

ABSTRACT

Machine learning (ML) is an artificial intelligence technique that allows systems to make decision or prediction by learning from experience independently based on large data sets. It is efficient for data analysis and information extraction when the data sets are big and complicated, which makes it an ideal tool to help study epigenetic changes like DNA methylation. DNA methylation, one of the major epigenetic processes that mediate gene expression without changing DNA sequences, is an important causative factor of many malignant transformations like cancers and aging. ML applications in studying DNA methylation patterns and identifying genomic regions susceptible to DNA methylation changes have brought great promises for disease diagnosis and personalized treatment. By reviewing the cases of ML algorithms applied in DNA methylation study, we derive a brief developmental history of how different ML techniques had been used to extract more and more useful information about DNA methylation, which provides insights for future ML application improvement.

KEYWORDS

Machine learning, DNA methylation, data analysis, information extraction, epigenetic changes