Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

ABSTRACT: RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a c

SUBMITTER: Liang Y 

PROVIDER: S-EPMC10929084 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature