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GPEC: A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection

Paper ID Volume ID Publish Year Pages File Format Full-Text
15181 1386 2012 7 PDF Available
Title
GPEC: A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection
Abstract

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.

Keywords
Cytoscape plug-in; Gene prioritization; Random walk with restart algorithm; Biomedical evidence collection
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GPEC: A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 37, April 2012, Pages 17–23
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us