| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 15181 | Computational Biology and Chemistry | 2012 | 7 Pages |
Finding genes associated with a disease is an important issue in the biomedical area and many gene prioritization methods have been proposed for this goal. Among these, network-based approaches are recently proposed and outperformed functional annotation-based ones. Here, we introduce a novel Cytoscape plug-in, GPEC, to help identify putative genes likely to be associated with specific diseases or pathways. In the plug-in, gene prioritization is performed through a random walk with restart algorithm, a state-of-the art network-based method, along with a gene/protein relationship network. The plug-in also allows users efficiently collect biomedical evidence for highly ranked candidate genes. A set of known genes, candidate genes and a gene/protein relationship network can be provided in a flexible way.
Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlight► GPEC is based on random walk with restart algorithm, a state-of-the art algorithm. ► GPEC can prioritize candidate genes of a disease or pathway. ► GPEC can collect effectively biomedical evidence for putative genes. ► GPEC provide a flexible input of known/candidate genes and network of genes/proteins. ► GPEC is user-friendly with various miscellaneous functions.
