Name
Title | ||
|---|---|---|
Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs |
Abstract | ||
|---|---|---|
Since abnormal expression of long noncoding RNAs (lncRNAs) is often closely related to various human diseases, identification of disease-associated lncRNAs is helpful for exploring the complex pathogenesis. Most of recent methods concentrate on exploiting multiple kinds of data related to lncRNAs and diseases for predicting candidate disease-related lncRNAs. These methods, however, failed to deeply integrate the topology information from the meta-paths that are composed of lncRNA, disease and microRNA (miRNA) nodes. We proposed a new method based on fully connected autoencoders and convolutional neural networks, called ACLDA, for inferring potential disease-related lncRNA candidates. A heterogeneous graph that consists of lncRNA, disease and miRNA nodes were firstly constructed to integrate similarities, associations and interactions among them. Fully connected autoencoder-based module was established to extract the low-dimensional features of lncRNA, disease and miRNA nodes in the heterogeneous graph. We designed the attention mechanisms at the node feature level and at the meta-path level to learn more informative features and meta-paths. A module based on convolutional neural networks was constructed to encode the local topologies of lncRNA and disease nodes from multiple meta-path perspectives. The comprehensive experimental results demonstrated ACLDA achieves superior performance than several state-of-the-art prediction methods. Case studies on breast, lung and colon cancers demonstrated that ACLDA is able to discover the potential disease-related lncRNAs. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bib/bbac089 | BRIEFINGS IN BIOINFORMATICS |
Keywords | DocType | Volume |
topology learning based on meta-paths, meta-path level attention mechanism, node feature level attention mechanism, low-dimensional representation learning, lncRNA-disease association prediction | Journal | 23 |
Issue | ISSN | Citations |
3 | 1467-5463 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Xuan | 1 | 409 | 32.37 |
| Zhe Gong | 2 | 0 | 0.34 |
| Hui Cui | 3 | 7 | 8.76 |
| Bochong Li | 4 | 0 | 0.34 |
| Tiangang Zhang | 5 | 2 | 5.78 |