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Issue 3, 2016
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GTA: a game theoretic approach to identifying cancer subnetwork markers

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Abstract

The identification of genetic markers (e.g. genes, pathways and subnetworks) for cancer has been one of the most challenging research areas in recent years. A subset of these studies attempt to analyze genome-wide expression profiles to identify markers with high reliability and reusability across independent whole-transcriptome microarray datasets. Therefore, the functional relationships of genes are integrated with their expression data. However, for a more accurate representation of the functional relationships among genes, utilization of the protein–protein interaction network (PPIN) seems to be necessary. Herein, a novel game theoretic approach (GTA) is proposed for the identification of cancer subnetwork markers by integrating genome-wide expression profiles and PPIN. The GTA method was applied to three distinct whole-transcriptome breast cancer datasets to identify the subnetwork markers associated with metastasis. To evaluate the performance of our approach, the identified subnetwork markers were compared with gene-based, pathway-based and network-based markers. We show that GTA is not only capable of identifying robust metastatic markers, it also provides a higher classification performance. In addition, based on these GTA-based subnetworks, we identified a new bonafide candidate gene for breast cancer susceptibility.

Graphical abstract: GTA: a game theoretic approach to identifying cancer subnetwork markers

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Publication details

The article was received on 11 Oct 2015, accepted on 23 Dec 2015 and first published on 23 Dec 2015


Article type: Paper
DOI: 10.1039/C5MB00684H
Citation: Mol. BioSyst., 2016,12, 818-825
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    GTA: a game theoretic approach to identifying cancer subnetwork markers

    S. Farahmand, S. Goliaei, N. Ansari-Pour and Z. Razaghi-Moghadam, Mol. BioSyst., 2016, 12, 818
    DOI: 10.1039/C5MB00684H

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