Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Epigenetics regulates gene expression via DNA methylation, histone modifications, and non-coding RNA.
• Epigenetic alterations are linked to diseases like cancer and congenital deformities.
• Traditional experimental epigenetics research is time-consuming and costly.

Purpose of the Study:

• To review AI models applied to epigenomic data for understanding gene regulation and disease.
• To provide a taxonomy of epigenetics problems for AI researchers.
• To suggest AI solutions for epigeneticists and identify research gaps.

Main Methods:

• Narrative review of published research on AI in epigenetics.
• Analysis of AI models trained on epigenomic data.
• Categorization of AI applications in epigenetics research.

Main Results:

• AI effectively predicts disease markers, gene expression, enhancer-promoter interactions, and chromatin states.
• Identified various AI approaches applicable to specific epigenetics problems.
• Highlighted existing gaps, challenges, and future research directions in AI-driven epigenetics.

Conclusions:

• AI and machine learning offer powerful tools to overcome limitations in experimental epigenetics research.
• Bridging AI expertise and epigenetics knowledge is crucial for advancing the field.
• Future research should focus on addressing identified challenges to fully leverage AI in epigenetics.