Integrating BERT pre-training with graph common neighbours for predicting ceRNA interactions.

ABSTRACT:

Introduction

Predicting interactions between microRNAs (miRNAs) and competing endogenous RNAs (ceRNAs), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), is essential for understanding gene regulation. With the development of Graph Neural Networks (GNNs), existing works have demonstrated the ability to capture information from miRNA-ceRNA interactions to predict unseen associations. However, current deep GNNs only leverage node-node pairwise features, neglecting the information inherent in the RNA chains themselves, as different RNAs possess chains of varying lengths.

Methods

To address this issue, we propose a novel model termed the BERT-based ceRNA Graph Predictor (BCGP), which leverages both RNA sequence information and the heterogeneous relationships

SUBMITTER: Xie Z 

PROVIDER: S-EPMC12441610 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature