Estimating functional coupling between cancer gene sub-networks using novel interaction measures
by Rama Srikanth Mallavarapu; Tae-Jin Ahn; Subhankar Mukherjee; Ajit S. Bopardikar; Garima Agarwal; Taesung Park
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 15, No. 1, 2016

Abstract: Cancer is a heterogeneous disease in that a single therapy may not be suitable for every patient. This is because, even within a single cancer, gene-level aberrations vary across patients. However, when genes are viewed at sub-network level, it is observed that these aberrations occur in sub-networks that are conserved across patients. Investigation of cancer gene sub-networks is therefore expected to yield greater insights. Also, multiple sub-networks could be involved in propagation of cancer and therefore coupling between them is of great importance. We present a method to measure and quantify coupling between two given sub-networks. For this we present interaction measures based on connecting genes and common neighbourhoods between sub-networks. We demonstrate this method using breast cancer focused around MET pathway and discuss functional coupling of various associated sub-networks. Studies based on such methods will play an important role in patient stratification and development of effective targeted therapies.

Online publication date: Thu, 21-Apr-2016

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