From biogenesis to deep modeling: a holistic review of miRNA–disease prediction computational methods with experimental comparison

Abnormal dysregulation of microRNAs (miRNAs) expression may lead to a wide spectrum of diseases, and as miRNAs play pivotal roles in disease pathogenesis, diagnosis, and therapy, identifying potential miRNA–disease associations (MDAs) is essential for discovering new diagnostic biomarkers, developing targeted therapeutic strategies, and advancing personalized medicine. Traditional wet-lab experiments are time-consuming, expensive, and consume a lot of resources. Hence, various computational approaches should be developed as auxiliary a priori tools. In the following text, we compile different methods proposed for MDA prediction over the past decade. First, we analyze the data resources supporting MDA studies and introduce approaches for quantifying similarities among MDAs. Second, we comprehensively review 66 computational methods, classify them into five categories, and present comparative experimental analyses on selected methods to identify future research directions. To enhance accessibility, we upload a summary of discussed methods to a GitHub repository (https://github.com/xiesiya/miRNA-disease-association-methods). This review provides comprehensive background knowledge on computational methods for future MDA prediction research.