Multi-Objective Approach for Identifying Cancer Subnetwork Markers

Abstract

AbstractIdentifying genetic markers for cancer is one of the main challenges in the recent researches. Between different cohorts of genetic markers such as genes or a group of genes like pathways or sub-network, identifying functional modules like subnetwork markers has been more challenging. Network-based classification methods have been successfully used for finding effective cancer subnetwork markers. Combination of metabolic networks and molecular profiles of tumor samples has led researchers to a more accurate prediction of subnetwork markers. However, topological features of the network have not been considered in the activity of the subnetwork. Here, we apply a novel protein-protein interaction network-based classification method that considers topological features of the network in addition to the expression profiles of the samples. We have considered the problem of identifying cancer subnetwork markers as a multi-objective problem which in this approach, each subnetwork’s activity level is measured according to both objectives of the problem; Differential expression level of the genes and topological features of the nodes in the network. We found that the subnetwork markers identified by this method achieve higher performance in the classification of cancer outcome in comparison to the other subnetwork markers.