Enhancing RBP Binding Site Prediction on Long RNA Sequences through Large Language Models

RNA binding proteins (RBPs) are crucial in various biological processes and gene regulation. Accurately predicting RBP binding sites is essential for advancing research in biology and medicine. Despite the success of deep learning models in predicting these sites, many RNA sequences exceed the manageable length for existing models. These models typically segment long RNA sequences into shorter sections to predict protein binding, which disrupts RNA structure and loses sequence information. In this study, we develop a language model that processes long-sequence RNA without segmentation, maintaining sequence integrity. Inspired by the Enformer model, we use 128 bases as a token, enabling the processing of RNA sequences up to 20,480 base pairs. This model effectively learns and analyzes the interactions within long sequences, significantly enhancing RBP binding site prediction. We perform three prediction tasks: whole sequence prediction, segmented multi-label prediction, and segmented single-label prediction, achieving promising results. Additionally, we address the issue of imbalanced positive and negative sample distribution in our dataset, exploring methods to mitigate its impact on model classification performance.