Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.

ABSTRACT: RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein-RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches:

SUBMITTER: Vaculik O 

PROVIDER: S-EPMC10604046 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature