RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.

ABSTRACT: RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned o

SUBMITTER: Peng X 

PROVIDER: S-EPMC9294422 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature