Research on Deep Learning Algorithms for Predicting DNA-Binding Proteins Based on Sequence Information

DNA-binding proteins play a crucial role in gene regulation and biological functions, making their accurate prediction essential for understanding biological processes and developing new drugs. Traditional DNA-binding protein prediction methods rely on sequence features and machine learning algorithms; however, these approaches often face challenges in accuracy and generalization when dealing with complex sequence information. To address this issue, this study proposes a deep learning-based prediction model that effectively captures both spatial and temporal features of sequences by combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Experimental results demonstrate that the proposed model performs excellently on multiple benchmark datasets, significantly improving prediction accuracy and robustness. This research offers a new approach to DNA-binding protein prediction and highlights the immense potential of deep learning in bioinformatics.