Advancing Disease Diagnosis and Biomarker Discovery: The Role of Machine Learning in ncRNA Analysis

Non-coding RNAs (ncRNAs) are critical regulators in diverse biological processes and pathological mechanisms. Their prospective utility as biomarkers for early disease detection and prognostic evaluation has attracted substantial scholarly interest. However, the complexity and volume of ncRNA data pose challenges in biomarker discovery. Recent advancements in machine learning have provided powerful tools to address these challenges, offering novel approaches for the analysis of ncRNA data. This review explores the integration of machine learning techniques in ncRNA research, focusing on miRNA, circRNA, and lncRNA. We discuss the biological functions of these ncRNAs, traditional methods for their analysis, and how machine learning enhances the discovery of disease biomarkers. Through a detailed examination of case studies, we highlight successful applications of machine learning in identifying ncRNA biomarkers. Additionally, we compare the effectiveness of various machine learning methods across different ncRNA types and address current challenges and future directions in the field. Our review underscores the transformative potential of machine learning in advancing ncRNA biomarker discovery, ultimately contributing to improved diagnostic and therapeutic strategies.