REVIEW article
Front. Genet.
Sec. Human and Medical Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1603687
This article is part of the Research TopicGenetic Horizons: Exploring Genetic Biomarkers in Therapy and Evolution with the Aid of Artificial IntelligenceView all 5 articles
Machine learning tools for deciphering the regulatory logic of enhancers in health and disease
Provisionally accepted- 14th Department of Internal Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- 2First Department of Pediatrics, National and Kapodistrian University of Athens, Athens, Greece
- 3Department of Obstetrics and Gynaecology, School of Medicine, University of Patras, Patras, Greece
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Transcriptional enhancers are DNA regulatory elements that control the levels and spatiotemporal patterns of gene expression during development, homeostasis, and pathophysiological processes. Enhancer identification and characterization at the genome-wide scale rely on their structural characteristics, such as chromatin accessibility, binding of transcription factors and cofactors, activating histone modifications, 3D interactions with other regulatory elements, as well as functional characteristics measured by massively parallel reporter assays (MPRAs) and sequence conservation approaches. Recently, machine learning approaches and particularly deep learning models (Enformer, BPNet, DeepSTARR, etc.) allow the prediction of enhancers, the impact of variants on their activity and the inference of transcription factor binding sites, leading, among others, to the construction of the first completely synthetic enhancers. We present the above computational tools and discuss their diverse applications towards cracking the enhancer regulatory code, which could have far-reaching ramifications for uncovering essential regulatory mechanisms and diagnosing and treating diseases. With an emphasis on sepsis, a leading cause of morbidity and mortality in hospitalized patients, we discuss computational approaches to identify sepsis-associated endotypes, circuits, and immune cell states and signatures characteristic of this condition, which could aid in developing novel therapies.
Keywords: deep learning, enhancers, Genomics, machine learning, Sepsis, Transcriptional regulation
Received: 31 Mar 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Foutadakis, Bourika, Styliara, Koufargyris, Safarika and Karakike. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Spyros Foutadakis, 4th Department of Internal Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, 124 62, Greece
Eleni Karakike, 4th Department of Internal Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, 124 62, Greece
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