Improved prediction of DNA and RNA binding proteins with deep learning models.

ABSTRACT: Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in essential biological processes. To facilitate functional annotation and accurate prediction of different types of NABPs, many machine learning-based computational approaches have been developed. However, the datasets used for training and testing as well as the prediction scopes in these studies have limited their applications. In this paper, we developed new strategies to overcome these limitations by generating more accurate and robust datasets and developing deep learning-based methods including both hierarchical and multi-class approaches to predict the types of NABPs for any given protein. The deep learning models employ two layers of convolutional neur

SUBMITTER: Wu S 

PROVIDER: S-EPMC11163377 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature