Circulating MicroRNAs as Biomarkers for Cardiovascular Events: Bioinformatics and Machine Learning Approaches

MicroRNAs (miRNAs) have emerged as key regulators of gene expression, influencing various biological processes, including those implicated in cancer and cardiovascular diseases (CVDs). These small, noncoding RNAs modulate gene functions by targeting messenger RNAs, thereby playing a critical role in disease progression. Increasing evidence suggests that circulating miRNAs hold great promise as minimally invasive biomarkers for early detection and risk assessment of cardiovascular conditions. This chapter provides a comprehensive overview of protocols for leveraging circulating miRNAs as predictive biomarkers for cardiovascular events, with a particular focus on postoperative atrial fibrillation (POAF). We outline methodologies encompassing sample collection, miRNA extraction, data preprocessing, biomarker identification, and predictive modeling using bioinformatics and machine learning approaches. By integrating computational and experimental techniques, we highlight strategies to enhance early cardiovascular disease detection and discuss challenges related to standardization and clinical translation of miRNA-based diagnostics.