Machine Learning in RNA and Chromatin Dynamics
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biology".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 53
Special Issue Editor
Interests: machine learning; AI; RNA structure; post-transcription regulation; co-transcription regulation; translation; degradation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The intricate three-dimensional organisation of chromatin and the dynamic regulation of RNA networks represent fundamental mechanisms that orchestrate gene expression and cellular identity. However, deciphering the complex regulatory “code” governing these molecular interactions remains one of biology’s greatest challenges. Recent breakthroughs in machine learning have revolutionised our ability to decode hidden patterns within chromatin architecture and RNA dynamics, offering unprecedented insights into how cells regulate essential biological processes. Foundation models trained on vast genomic and epigenomic datasets are now revealing subtle structural motifs and regulatory principles that were previously beyond experimental detection. These advancements hold immense promise for addressing fundamental questions in biology and medicine, from understanding disease mechanisms to developing precision therapeutic strategies.
This Special Issue aims to highlight innovative machine learning research that elucidates the intricate regulatory networks governing chromatin dynamics and RNA biology. We encourage submissions that explore novel machine learning methodologies and uncover functional patterns or “biological rules” underlying RNA and chromatin dynamics. Studies may encompass machine learning models for 3D genome structure prediction, interpretation, and design; RNA function analysis and prediction; multi-omics integration frameworks; and large language models for genomic sequence interpretation. Additionally, we welcome research on machine learning approaches for chromatin accessibility prediction and RNA structure prediction. By showcasing both theoretical advances and discoveries in fundamental biological knowledge, this collection seeks to deepen our understanding of cellular regulatory mechanisms, with the potential to inform future biomedical applications and therapeutic innovations.
Dr. Haopeng Yu
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- chromatin architecture
- RNA dynamics
- genomic foundation models
- computational biology
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