Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.

IF 7.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Briefings in bioinformatics Pub Date : 2025-08-31 DOI:10.1093/bib/bbaf468

Aymane Aghziel, Mohamed Adnane Mahraz, Hamid Tairi, Noura Aherrahrou

引用次数: 0

Abstract

This paper offers a comprehensive review of the synergy between artificial intelligence and DNA methylation analysis, encompassing machine learning, deep learning, natural language processing, and explainable artificial intelligence. In this study, we also highlighted the underexplored potential of signal processing and large language models-based models in DNA methylation research. Additionally, we discussed the challenges and limitations faced when managing and analyzing large and complex DNA methylation datasets. Furthermore, this article tries to shed light on the continuing evolution of this field and on the possible directions for future research.

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综合DNA甲基化分析的人工智能：概述、挑战和未来方向。

本文全面回顾了人工智能和DNA甲基化分析之间的协同作用，包括机器学习、深度学习、自然语言处理和可解释的人工智能。在这项研究中，我们还强调了信号处理和基于大型语言模型的模型在DNA甲基化研究中的潜力。此外，我们讨论了管理和分析大型和复杂的DNA甲基化数据集时面临的挑战和限制。此外，本文试图阐明该领域的持续发展和未来研究的可能方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Briefings in bioinformatics 生物-生化研究方法

CiteScore

13.20

自引率

13.70%

发文量

549

审稿时长

6 months

期刊介绍： Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.