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CytoGTA: A cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach.


ABSTRACT: In recent years, analyzing genome-wide expression profiles to find genetic markers has received much attention as a challenging field of research aiming at unveiling biological mechanisms behind complex disorders. The identification of reliable and reproducible markers has lately been achieved by integrating genome-scale functional relationships and transcriptome datasets, and a number of algorithms have been developed to support this strategy. In this paper, we present a promising and easily applicable tool to accomplish this goal, namely CytoGTA, which is a Cytoscape plug-in that relies on an optimistic game theoretic approach (GTA) for identifying subnetwork markers. Given transcriptomic data of two phenotype classes and interactome data, this plug-in offers discriminative markers for the two classes. The high performance of CytoGTA would not have been achieved if the strategy of GTA was not implemented in Cytoscape. This plug-in provides a simple-to-use platform, convenient for biological researchers to interactively work with and visualize the structure of subnetwork markers. CytoGTA is one of the few available Cytoscape plug-ins for marker identification, which shows superior performance to existing methods.

SUBMITTER: Farahmand S 

PROVIDER: S-EPMC5624584 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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CytoGTA: A cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach.

Farahmand S S   Foroughmand-Araabi M H MH   Goliaei S S   Razaghi-Moghadam Z Z   Razaghi-Moghadam Z Z  

PloS one 20171002 10


In recent years, analyzing genome-wide expression profiles to find genetic markers has received much attention as a challenging field of research aiming at unveiling biological mechanisms behind complex disorders. The identification of reliable and reproducible markers has lately been achieved by integrating genome-scale functional relationships and transcriptome datasets, and a number of algorithms have been developed to support this strategy. In this paper, we present a promising and easily ap  ...[more]

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