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Bioinformatics. 2020 Jan 6. pii: btz965. doi: 10.1093/bioinformatics/btz965. [Epub ahead of print]

Neural Inductive Matrix Completion with Graph Convolutional Networks for miRNA-disease Association Prediction.

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School of Software, Yunnan University, Kunming, China.



Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA.


We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks (GCNs) to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The AUC results showed that our method is significantly superior to existing methods. Furthermore, 50, 47, and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma, and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs.



Supplementary data are available at Bioinformatics online.

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