Fig. 1
From: GSNFS: Gene subnetwork biomarker identification of lung cancer expression data
GSNFS framework for gene subnetwork identification. For each gene-set, the expression data was integrated with network data to identify gene subnetwork biomarker for phenotype outcome classification. The improvement of the existing algorithm focused on subnetwork expansion procedure to aggregate significant genes. Two searching methods (GS and PN search) were implemented as searching algorithm in the GSNFS method. GS search treats seed nodes in the ith iteration as a set of the current subnetwork (i, ii) and searches all neighbours of the current subnetwork while PS search only looks for neighbours of a particular gene member in the current subnetwork (i or ii) bypassing genes that are already accounted for the current subnetwork (in this diagram is gene (i) and gene (ii))