A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Reconstruction of Heterogeneous Networks
2.1.1. The Human Protein–Protein Interactions
2.1.2. Disease–Gene Network
2.1.3. miRNA–Gene Network
2.1.4. miRNA–Disease Network
2.2. Raw Feature Extraction
2.3. Graph Convolution Network
2.4. Heterogeneous Graph Convolutional HGCNMDA Approach
2.4.1. HGCNMDA Convolution Layer and Negative Sampling
2.4.2. Edge Features Extraction
2.4.3. HGCNMDA Model Training
3. Results
3.1. Overall Performance
3.2. Performance of Model on Diseases
3.3. Comparison To Other Algorithms
3.4. Prediction of New miRNA–Disease Associations
4. Discussion and Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Model Baselines | Accuracy | AUROC | AUPRC |
---|---|---|---|
HGCNMDA (One-hot) | 0.8497 | 0.9036 | 0.8776 |
HGCNMDA (Node2vec/64) | 0.8626 | 0.9358 | 0.9290 |
HGCNMDA (Node2vec/128) | 0.9108 | 0.9626 | 0.9660 |
HGCNMDA (Node2vec/256) | 0.9116 | 0.9651 | 0.9689 |
Osteosarcoma | Polycystic Ovary Syndrome | ||
---|---|---|---|
miRNA | Evidence | miRNA | Evidence |
hsa-mir-26b | dbDEMCv2.0 | hsa-mir-9 | (Sørensen et al., 2014) [50] |
hsa-mir-218 | Unconfirmed | hsa-mir-21 | (Sørensen et al., 2014) [50] |
hsa-mir-873 | Unconfirmed | hsa-mir-155 | (Sørensen et al., 2014) [50] |
hsa-mir-383 | dbDEMCv2.0 | hsa-mir-146a | (Sørensen et al., 2014) [50] |
hsa-mir-16 | dbDEMCv2.0 | hsa-mir-223 | (Chuang et al., 2015) [51] |
hsa-mir-199a | dbDEMCv2.0 | hsa-mir-34a | Unconfirmed |
hsa-mir-671 | dbDEMCv2.0 | hsa-mir-145 | (Cai et al., 2017) [52] |
hsa-mir-367 | dbDEMCv2.0 | hsa-mir-126 | Unconfirmed |
hsa-mir-145 | dbDEMCv2.0 | hsa-mir-210 | Unconfirmed |
hsa-mir-17 | dbDEMCv2.0 | hsa-mir-32 | (Roth et al., 2014) [53] |
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Li, C.; Liu, H.; Hu, Q.; Que, J.; Yao, J. A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks. Cells 2019, 8, 977. https://doi.org/10.3390/cells8090977
Li C, Liu H, Hu Q, Que J, Yao J. A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks. Cells. 2019; 8(9):977. https://doi.org/10.3390/cells8090977
Chicago/Turabian StyleLi, Chunyan, Hongju Liu, Qian Hu, Jinlong Que, and Junfeng Yao. 2019. "A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks" Cells 8, no. 9: 977. https://doi.org/10.3390/cells8090977