MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features.

ABSTRACT: RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m⁶A, m⁵C, m¹A and so on. T

SUBMITTER: Wang L 

PROVIDER: S-EPMC10877979 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature