Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework.

ABSTRACT:

Background

Cancer is a complex disease where molecular mechanism remains elusive. A systems approach is needed to integrate diverse biological information for the prognosis and therapy risk assessment using mechanistic approach to understand gene interactions in pathways and networks and functional attributes to unravel the biological behaviour of tumors.

Results

We weighted the functional attributes based on various functional properties observed between cancerous and non-cancerous genes reported from literature. This weighing schema was then encoded in a Boolean logic framework to rank differentially expressed genes. We have identified 17 genes to be differentially expressed from a total of 11,173 genes, where ten genes are reported to be down-regulated via epigenetic inac

SUBMITTER: Kumar G 

PROVIDER: S-EPMC3605242 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature