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Decoding Natural Behavior from Neuroethological Embedding

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Published on: October 3, 2025

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

Zhengxing Xie¹, Tianping Ying², Ge Jing²

¹Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.

Frontiers in Genetics

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces the BERT-based ceRNA Graph Predictor (BCGP) to accurately predict microRNA (miRNA) interactions with long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) by integrating sequence and graph data.

Keywords:

ceRNA circRNA graph neural network lncRNA miRNA pre-train

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Predicting microRNA (miRNA)-competing endogenous RNA (ceRNA) interactions is crucial for understanding gene regulation.
Existing Graph Neural Networks (GNNs) for miRNA-ceRNA prediction overlook RNA sequence information.

Purpose of the Study:

To develop a novel model, the BERT-based ceRNA Graph Predictor (BCGP), for enhanced miRNA-ceRNA association prediction.
To integrate RNA sequence information with graph-based interaction data.

Main Methods:

Utilized a transformer-based model to generate contextualized RNA sequence representations.
Enriched the RNA interaction graph with sequence-derived features.
Employed the Neural Common Neighbour (NCN) technique for refined node feature extraction.

Main Results:

BCGP significantly outperformed existing methods on lncRNA-miRNA and circRNA-miRNA association prediction tasks.
Achieved higher accuracy in predicting both miRNA-lncRNA and miRNA-circRNA interactions across real-world datasets.

Conclusions:

Integrating RNA sequence information with graph-based interactions improves miRNA-ceRNA association prediction accuracy.
BCGP offers a valuable computational tool for dissecting complex gene regulatory networks.