Epigenomic Biomarker Discovery from Biomedical Literature: AI and Text Mining Toward Health Monitoring Frameworks

Abstract

Epigenomic regulation, particularly DNA methylation, plays a critical role in gene expression control and has emerged as an important source of biomarkers for disease diagnosis, risk prediction, and longitudinal health monitoring. As high-throughput sequencing technologies have expanded, epigenomic research has rapidly grown, producing a large and complex body of biomedical literature. This review presents an AI-driven literature-level analysis aimed at uncovering structural patterns and research trends related to epigenomic biomarker discovery. Using a large corpus of full-text articles collected from PubMed and PubMed Central, we applied text mining techniques including keyword frequency analysis, document-level co-occurrence analysis, topic clustering, contextual concordance analysis, and temporal trend analysis. Rather than evaluating individual experiments, this approach examines the broader research landscape to identify recurring conceptual structures and methodological patterns. The analysis reveals that epigenomic biomarker research is organized into several interconnected domains, including disease-focused epigenomics, chromatin regulation studies, transcriptomic integration research, and cancer-related epigenomic investigations. The rapid growth of publications since 2010 further reflects the increasing importance of high-throughput epigenomic profiling and biomarker-driven research. These findings demonstrate that AI-driven literature mining provides a scalable framework for uncovering epigenomic biomarker knowledge and translating it toward AI-enabled health monitoring systems. Such approaches may support biomarker prioritization, early disease detection, and data-driven health monitoring within precision health environments.