Name
Title | ||
|---|---|---|
Predicting Disease-related RNA Associations based on Graph Convolutional Attention Network |
Abstract | ||
|---|---|---|
Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are time-consuming and labor-intensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential disease-RNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other disease-RNAs association prediction methods, GCAN operates the computation process from global structure of disease-RNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in disease-RNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different disease-RNAs networks: disease-miRNA and disease-lncRNA. Comparisons of several state-of-the-art methods using disease-RNAs networks show that our novel frameworks outperform baselines by a wide margin in potential disease-RNAs associations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBM47256.2019.8983191 | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
disease-related association,network representation,attention mechanism,graph convolution networks | Graph,Disease,Global structure,Computation process,Computer science,Convolution,Attention network,Artificial intelligence,Deep learning,Machine learning | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-7281-1868-0 | 2 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinli Zhang | 1 | 2 | 1.05 |
| Xiaohua Hu | 2 | 2819 | 314.15 |
| Zongli Jiang | 3 | 2 | 1.39 |
| Bo Song | 4 | 7 | 4.15 |
| Wei Quan | 5 | 259 | 30.09 |
| Zheng Chen | 6 | 9 | 2.82 |