Epigenetic biomarkers in inflammatory bowel diseases—computational challenges and opportunities

Inflammatory bowel diseases (IBD) remain a therapeutic challenge due to their heterogeneous nature and the absence of clinically actionable biomarkers to guide precision treatment. While multi-omics studies have advanced our understanding of the disease, meaningful translation into practice has been hindered by lack of validation and a need to move from association to a more generalizable signal. Epigenetics, particularly DNA methylation, offers a promising lens to capture disease-relevant regulatory states that reflect both genetic predisposition and environmental influences. However, the path to clinical adoption has been limited by persistent computational hurdles. Here we synthesize the current landscape of IBD biomarker discovery and highlight the conceptual advantages of epigenetic signatures. We outline the main obstacles that have limited clinical translation to date, including data heterogeneity, batch effects, and the challenge of distinguishing functional "driver" from non-functional "passenger" epigenetic changes. We then discuss how recent advances in computational methodology-spanning data harmonization, integrative modeling, and interpretable machine learning-can help bridge the gap between complex datasets and reliable, deployable biomarkers. Finally, we propose a forward-looking roadmap for study design and validation aimed at moving the field toward routine clinical implementation, thereby realizing the full potential of epigenetics in IBD.