AI for Epigenetics

Abstract

This textbook module provides a rigorous academic introduction to the intersection of artificial intelligence and epigenetic science. Beginning with the historical foundations of genetic inheritance and the discovery of epigenetic regulation, the text systematically examines the core molecular mechanisms governing gene expression — including DNA methylation, histone modification, non-coding RNA activity, and chromatin remodeling — before turning to the environmental, nutritional, psychological, and behavioral factors that shape the epigenome across the lifespan. Subsequent chapters address transgenerational epigenetic inheritance, the epigenetic basis of cancer, neurodegenerative disease, autoimmune disorders, and metabolic conditions, followed by a detailed treatment of the computational tools and Python libraries used in modern epigenomic research. The text then surveys active AI applications in the field, including DNA methylation biomarker discovery, epigenetic clock development, single-cell epigenomics, AI-assisted epigenome editing, and personalized medicine. Final chapters examine the ethical, regulatory, and societal dimensions of AI-driven epigenetic technologies, including data privacy, algorithmic bias, equity of access, and emerging international governance frameworks. The module includes a full glossary of key terms and a reference list of 26 peer-reviewed publications. It is intended for students, researchers, educators, and policymakers engaged with the rapidly evolving intersection of genomic science and artificial intelligence. This is Module 1 in the AI Topics series by Xavier Honablue, M.Ed.

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