AI-Driven Genomic Biomarker Discovery for Precision Diagnosis and Personalized Treatment

Abstract

The identification of genomic biomarkers has become a cornerstone for precision medicine, enabling early disease diagnosis, prognosis prediction, and personalized treatment strategies. Recent advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have demonstrated substantial potential in analyzing high-dimensional genomic datasets to uncover novel biomarkers. AI algorithms can integrate heterogeneous data types, such as gene expression, single nucleotide polymorphisms (SNPs), and epigenetic modifications, to identify patterns associated with specific diseases. This study reviews current methodologies and applications of AI in genomic biomarker discovery, highlighting successes in oncology, cardiovascular disorders, and neurodegenerative diseases. Key AI approaches, including random forests, support vector machines, convolutional neural networks, and autoencoders, are employed to improve predictive accuracy and reduce false discovery rates. The study also discusses challenges, such as data sparsity, interpretability, and computational resource requirements. Additionally, we examine case studies where AI-driven genomic biomarker identification has guided targeted therapy selection, improving patient outcomes. Finally, we outline future perspectives, emphasizing the integration of multi-omics data, real-time clinical data streams, and federated learning to enhance biomarker discovery across diverse populations. Overall, AI-driven genomic biomarker research holds transformative potential for accelerating personalized medicine, optimizing therapeutic interventions, and providing actionable insights into disease mechanisms.