AI-Driven Genomic Analysis for Identifying Novel Biomarkers in Precision Medicine

Abstract

This study explores the potential of artificial intelligence (AI)-driven genomic analysis in identifying novel biomarkers to advance the field of precision medicine. By leveraging machine learning and deep learning models across multi-omic datasets, including genomic, transcriptomic, and proteomic data, we demonstrate significant improvements in predictive accuracy, scalability, and biological interpretability compared to traditional biomarker discovery methods. The integration of heterogeneous data sources enabled the detection of subtle molecular patterns associated with disease progression and therapeutic response, while explainable AI techniques ensured that the results remained biologically meaningful and clinically relevant. Results indicate that AI-driven approaches reduced false discovery rates, improved classification of patient subgroups, and uncovered candidate biomarkers particularly valuable in oncology and rare disease contexts. Furthermore, the scalability of these models allows for application to large cohorts, facilitating the translation of biomarker discoveries into population-level precision health initiatives. The findings underscore the transformative role of AI in enabling patient stratification, targeted therapies, and personalized treatment strategies. While challenges related to algorithmic bias, data harmonization, and clinical validation persist, this research highlights that AI-driven biomarker discovery is an essential step toward realizing the full promise of predictive, preventive, and personalized medicine.