UNASSIGNED: The identification of genetic markers (e.g. genes, pathways and subnetworks) for @entity5 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 @entity5 subnetwork markers by integrating genome-wide expression profiles and PPIN. The GTA method was applied to three distinct whole-transcriptome @entity0 datasets to identify the subnetwork markers associated with @entity3 . 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 @entity0 susceptibility.

GTA: a game theoretic approach to identifying XXXX subnetwork markers.

@entity5 :: ('MESH:D009369', 'Disease') :: ['cancer'] @entity0 :: ('MESH:D001943', 'Disease') :: ['breast cancer'] @entity3 :: ('MESH:D009362', 'Disease') :: ['metastasis']
@entity5 :: (MESH:D009369,Disease) :: ['cancer']
