Name
Title | ||
|---|---|---|
Distant metastasis identification based on optimized graph representation of gene interaction patterns |
Abstract | ||
|---|---|---|
Metastasis is a major cause of cancer morbidity and mortality, and most cancer deaths are caused by cancer metastasis rather than by the primary tumor. The prediction of metastasis based on computational methods has not been explored much in the previous research. In this study, we proposed a graph convolutional network embedded with a graph learning (GL) module, named glmGCN, to predict the distant metastasis of cancer. Both the mRNA and lncRNA expressions were used to provide more genetic information than using the mRNA alone and we used them to construct gene interaction graph representation to consider the effect of genetic interaction. Then, the prediction of the cancer metastasis was performed under a GCN framework, which extracted informative and advanced features from the built non-regular graph structures. Particularly, a GL module was embedded in the proposed glmGCN to learn an optimal graph representation of the gene interaction. We firstly constructed the protein-protein interaction network to represent the initial gene(node) relationship graph. Then, through the GL module, a new graph representation was built which optimally learned the gene interaction strength. Finally, the GCN was adopted to identify the distant metastasis cases. It is worth mentioning that the proposed method pays more attentions on the gene-gene relation than the previous GCN-based method, so more accurate prediction performance can be obtained. The glmGCN was trained based on two types of cancer and was further validated using two other cancer types. A series of experiments have shown that the effectiveness of the proposed method. The implementation for the proposed method is available at https://github.com/RanSuLab/Metastasis-glmGCN. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bib/bbab468 | BRIEFINGS IN BIOINFORMATICS |
Keywords | DocType | Volume |
metastasis identification, graph learning, graph convolutional network, TCGA | Journal | 23 |
Issue | ISSN | Citations |
1 | 1467-5463 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Su | 1 | 0 | 0.34 |
| Yingying Zhu | 2 | 0 | 0.34 |
| quan zou | 3 | 558 | 67.61 |
| Leyi Wei | 4 | 100 | 17.06 |