Open AccessCytoGTA: A cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach
Author(s) -
Saman Farahmand,
Mohammad-Hadi Foroughmand-Araabi,
Sama Goliaei,
Zahra Razaghi-Moghadam
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0185016
Subject(s) - subnetwork , plug in , discriminative model , identification (biology) , computer science , computational biology , interactome , bioinformatics , biology , machine learning , genetics , gene , botany , computer security , programming language
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.