Support Vector Machine (SVM)-Based Review of RNA-RNA Interaction Prediction Methods

RNA-RNA interactions (RRIs) play a vital role in cellular processes including gene regulation, RNA editing, and translational control. Aberrant RRIs are increasingly recognized as contributors to cancer initiation, progression, and metastasis. Accurate computational prediction of RRIs therefore offers significant opportunities for cancer biomarker discovery, understanding tumor biology, and therapeutic target identification. Among machine learning approaches, support vector machines (SVMs) have been applied in RRI prediction due to their robustness in handling high-dimensional, nonlinear data and relatively small training datasets. This chapter reviews SVM-based methods for RRI prediction, highlights representative tools and strategies, and discusses current challenges and relevance to cancer application. Emerging trends that integrate SVMs with deep learning and experimental data augmentation are also outlined, pointing toward pathways for clinical translation.