Transfer Learning for Predicting ncRNA-Protein Interactions.

Abstract

Noncoding RNAs (ncRNAs) interact with proteins, playing a crucial role in regulating gene expression and cellular functions. Accurate prediction of these interactions is essential for understanding biological processes and developing novel therapeutic agents. However, identifying ncRNA-protein interactions (ncRPI) through experimental methods is often costly and time-consuming. Although numerous machine learning and deep learning approaches have been developed for ncRPI prediction, their accuracy is often limited by the small size of available data sets. To address this challenge, we present Transfer-RPI, a transfer learning-based framework designed to enhance generalization and improve prediction performance through deep feature learning. Transfer-RPI leverages the RiNALMo and ESM models to extract comprehensive features from RNA and protein sequences, respectively. By integrating these rich and informative feature sets, Transfer-RPI fine-tunes the embedded complex interaction patterns, thereby enhancing performance even when trained on small data sets. Our results demonstrate that deep learning architectures augmented with intricate feature representations and transfer learning significantly boost prediction accuracy. Under 5-fold cross-validation, Transfer-RPI outperforms existing methods, achieving accuracies of 80.1, 89.3, 94.3, 94.4, and 95.4% on the RPI369, RPI488, RPI1807, RPI2241, and NPInter v2.0 data sets, respectively. These findings highlight the potential of transfer learning to overcome data limitations and enhance prediction performance. By harnessing advanced feature representations, Transfer-RPI offers a powerful tool for uncovering ncRPI, paving the way for deeper insights into molecular biology and novel therapeutic innovations.