Graph convolutional network approach to discovering disease-related circRNA-miRNA-mRNA axes

Chengxin He, Lei Duan, Huiru Zheng, Jesse Li-ling, Linlin Song, Longhai Li

Research output: Contribution to journalArticlepeer-review

Abstract

Non-coding RNAs are gaining prominence in biology and medicine, as they play major roles in cellular homeostasis among which the circRNA-miRNA-mRNA axes are involved in a series of disease-related pathways, such as apoptosis, cell invasion and metastasis. Recently, many computational methods have been developed for the prediction of the relationship between ncRNAs and diseases, which can alleviate the time-consuming and labor-intensive exploration involved with biological experiments. However, these methods handle ncRNAs separately, ignoring the impact of the interactions among ncRNAs on the diseases. In this paper we present a novel approach to discovering disease-related circRNA-miRNA-mRNA axes from the disease-RNA information network. Our method, using graph convolutional network, learns the characteristic representation of each biological entity by propagating and aggregating local neighbor information based on the global structure of the network. The approach is evaluated using the real-world datasets and the results show that it outperforms other state-of-the-art baselines on most of the metrics.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalMethods
Early online date7 Nov 2021
DOIs
Publication statusE-pub ahead of print - 7 Nov 2021

Keywords

  • Disease-related association
  • circRNA-miRNA-mRNA axis
  • Graph convolutional network

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