LSTM-Based Methods for RNA-RNA Interaction Prediction

RNA-RNA interactions (RRIs) are central to various biological processes, including post-transcriptional regulation, gene silencing, and viral replication. While traditional computational methods for RRI prediction rely on sequence complementarity and thermodynamic models, deep learning approaches, particularly those leveraging long short-term memory (LSTM) networks, have shown great promise in modeling the complexity of RNA behavior. This chapter reviews recent advances in LSTM-based methods for RRI prediction, discusses the key datasets and evaluation metrics, and outlines future directions in this emerging field.