AUTHOR=Xu Peng , Li Chuchu , Yuan Jiaqi , Bao Zhenshen , Liu Wenbin TITLE=Predict lncRNA-drug associations based on graph neural network JOURNAL=Frontiers in Genetics VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1388015 DOI=10.3389/fgene.2024.1388015 ISSN=1664-8021 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.