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

2024/11/04 by Muhammad Tahir, Mahboobeh Norouzi, Shehroz S. Khan +4 · 1 voice · 35 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithm #Artificial intelligence #Biology #Computer science #Deep learning #Epigenetics #Epigenetics and DNA Methylation #Genetics #Genetics, Bioinformatics, and Biomedical Research #Machine Learning in Bioinformatics #Machine learning #Sequence (biology) #cs.AI #cs.LG #q-bio.GN

paper · pdf · open access · doi:10.1016/j.compbiomed.2024.109302

published in Computers in Biology and Medicine 183, 109302 (Elsevier BV)

openalex publication_date 2024/11/04 · arxiv published 2025/04/01 · arxiv updated 2025/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone modifications, chromatin conformation, and non-coding RNA. The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities. Over the last few decades, high-throughput experimental approaches have been used to identify and understand epigenetic changes, but these laboratory experimental approaches and biochemical processes are time-consuming and expensive. To overcome these challenges, machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations. In this paper we provide a narrative review of published research on AI models trained on epigenomic data to address a variety of problems such as prediction of disease markers, gene expression, enhancer-promoter interaction, and chromatin states. The purpose of this review is twofold as it is addressed to both AI experts and epigeneticists. For AI researchers, we provided a taxonomy of epigenetics research problems that can benefit from an AI-based approach. For epigeneticists, given each of the above problems we provide a list of candidate AI solutions in the literature. We have also identified several gaps in the literature, research challenges, and recommendations to address these challenges.

Citations

• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Auto-Encoding Variational Bayes
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• Long Short-Term Memory
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• The Mammalian Epigenome
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• Deep learning
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• SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression
• Gene regulation by long non-coding RNAs and its biological functions
• Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
• Variational Diffusion Models
• Reinforcement Learning: An Introduction
• Matheuristics: using mathematics for heuristic design
• Diffusion Models in Vision: A Survey
• Exploiting Generative AI to Scale up Intelligent Tutoring Systems
• AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism

Discussions

• Tahir, Norouzi, Khan, Davie, Yamanaka, Ashraf: Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts https://arxiv.org/abs/2 [bsky, 1 points, 1 comments]

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