Variational graph auto-encoders for miRNA-disease association prediction

•Variational graph auto-encoders are excellent for predicting miRNA-disease associations.•Graph convolutional networks obtain good representations for miRNAs and diseases.•Variational auto-encoders can deal with missing data in the miRNA-disease network.•Integrating different databases helps predict...

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Vydané v:Methods (San Diego, Calif.) Ročník 192; s. 25 - 34
Hlavní autori: Ding, Yulian, Tian, Li-Ping, Lei, Xiujuan, Liao, Bo, Wu, Fang-Xiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.08.2021
Predmet:
Algorithms
case studies
Computational Biology
Deep learning
Graph convolutional network
human diseases
Humans
microRNA
MicroRNAs - genetics
miRNA-disease association
Neural Networks, Computer
prediction
Variational autoencoder
Deep learning
Variational autoencoder
Graph convolutional network
miRNA-disease association
ISSN:1046-2023, 1095-9130, 1095-9130
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Shrnutí:•Variational graph auto-encoders are excellent for predicting miRNA-disease associations.•Graph convolutional networks obtain good representations for miRNAs and diseases.•Variational auto-encoders can deal with missing data in the miRNA-disease network.•Integrating different databases helps predict novel miRNA-disease associations. Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1046-2023
1095-9130
1095-9130
DOI:10.1016/j.ymeth.2020.08.004