PmiRtarbase: A positive miRNA-target regulations database

Xu, Peng; Li, Xianbin; Liang, Yuan; Bao, Zhenshen; Zhang, Fengyue; Li, Gu; Kosari, Saeed; Liu, Wenbin

doi:10.1016/j.compbiolchem.2022.107690

Cited by 6 publications

(1 citation statement)

References 42 publications

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“…We will integrate them as lncRNA feature vectors and drug feature vectors in the future. Finally, we plan to develop more efficient deep learning methods (Lin et al, 2023) based on more types of association networks (Xu et al, 2020;Xu et al, 2022) to improve the prediction performance of LDAs.…”

Section: Discussionmentioning

confidence: 99%

Predict lncRNA-drug associations based on graph neural network

Xu,

Li,

Yuan

et al. 2024

Front. Genet.

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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.

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Section: Discussionmentioning

confidence: 99%