Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks.

Background: Identifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug.

Method: In this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network.