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A novel network control model for identifying personalized driver genes in cancer

Fig 2

Overview of Paired-SSN for constructing personalized state transition networks.

For a given cancer patient, TCGA.EJ.7781 in Prostate Adenocarcinoma (PRAD) cancer data, we chose the expression data of all normal samples in PRAD as the reference data and respectively constructed the co-expression network of tumor sample (white color) and normal sample (green color) with the reference data by using SSN method. Then the personalized state transition network was constructed where the nodes represent the genes and edges denote the significant difference of gene interactions between normal sample and tumor sample of an individual patient in the disease development. Here we showed the individual specific sub-networks related with driver gene TP53 which contain its first-order neighboring genes as an example in this Fig.

Fig 2