Predict lncRNA-drug associations based on graph neural network

Peng Xu; Chuchu Li; Jiaqi Yuan; Zhenshen Bao; Wenbin Liu

doi:10.3389/fgene.2024.1388015

Predict lncRNA-drug associations based on graph neural network

Front Genet. 2024 Apr 26:15:1388015. doi: 10.3389/fgene.2024.1388015. eCollection 2024.

Authors

Peng Xu^{1

2}, Chuchu Li¹, Jiaqi Yuan¹, Zhenshen Bao³, Wenbin Liu^{1

4}

Affiliations

¹ Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
² School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China.
³ College of Information Engineering, Taizhou University, Taizhou, Jiangsu, China.
⁴ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong, China.

Abstract

LncRNAs are an essential type of non-coding RNAs, which have been reported to be involved in various human pathological conditions. Increasing evidence suggests that drugs can regulate lncRNAs expression, which makes it possible to develop lncRNAs as therapeutic targets. Thus, developing in-silico methods to predict lncRNA-drug associations (LDAs) is a critical step for developing lncRNA-based therapies. In this study, we predict LDAs by using graph convolutional networks (GCN) and graph attention networks (GAT) based on lncRNA and drug similarity networks. Results show that our proposed method achieves good performance (average AUCs > 0.92) on five datasets. In addition, case studies and KEGG functional enrichment analysis further prove that the model can effectively identify novel LDAs. On the whole, this study provides a deep learning-based framework for predicting novel LDAs, which will accelerate the lncRNA-targeted drug development process.

Keywords: drug discovery; graph attention networks; link prediction; lncRNA-drug association; principal component analysis.

Grants and funding

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Natural Science Foundation of China, grant numbers of 62072128 and 62002079; the Natural Science Foundation of Guangdong Province of China, grant number of 2023A1515011401.