Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.

ABSTRACT:

Background

Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.

Results

We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. Th

SUBMITTER: Dopazo J 

PROVIDER: S-EPMC5700672 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature