HGPEC

HGPEC can effectively predict novel disease-gene and disease-disease associations and support network- and rank-based visualization
**Background:** Finding gene-disease and disease-disease associations play important roles in the biomedical area and many prioritization methods have been proposed for this goal. Among them, approaches based on a heterogeneous network of genes and diseases are considered state-of-the-art ones, which achieve high prediction performance and can be used for diseases with/without known molecular basis. **Results:** Here, we developed a Cytoscape app, namely HGPEC, based on a random walk with restart algorithm on a heterogeneous network of genes and diseases. This app can prioritize candidate genes and diseases by employing a heterogeneous network consisting of a network of genes/proteins and a phenotypic disease similarity network. Based on the rankings, novel disease-gene and disease-disease associations can be identified. These associations can be supported with network- and rank-based visualization as well as evidences and annotations from biomedical data. A case study on prediction of novel breast cancer-associated genes and diseases shows the abilities of HGPEC. In addition, we showed prominence in the performance of HGPEC compared to other tools for prioritization of candidate disease genes. **Conclusions:** Taken together, our app is expected to effectively predict novel disease-gene and disease-disease associations and support network- and rank-based visualization as well as biomedical evidences for such the associations.

1.0.0.SNAPSHOT

Works with Cytoscape 3.3

Release Notes

For more detail, please see the user manual https://sites.google.com/site/duchaule2011/bioinformatics-tools/hgpec

CYTOSCAPE 3

Version 1.0.0.SNAPSHOT

Released 1 Jun 2017

Works with Cytoscape 3.3

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CYTOSCAPE 2

Version 1.0

Released 5 Apr 2017

Works with Cytoscape 2.7, 2.8

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