Versatility-preserving Multi-omics Data Analysis by Ranking the Nodes in Multilayer Network

Abstract

The determination of hub nodes in complex biological networks is important as they propagate major information and significantly interact with other nodes. The challenging problem is to identify those nodes in biological networked systems that are characterized by different types of genetic interactions, constructing a multilayer network. Here we describe an algorithm that allows us to calculate the degree centrality of each node in multiple networks and as a result, we rank the nodes. The main objective is to identify those nodes preserving the versatility in the multilayer network. These nodes play the most vital roles in the whole interconnected structure, connecting together various types of relations. To demonstrate the effectiveness of the proposed approach, gene (mRNA) expression and methylation data are integrated to identify the most influential nodes. In this regard, a novel method is proposed that ranks the nodes on the basis of their score in the multilayer network. The ranking method imports a weight for every node. This study focuses on feature selection by integrating multiple data in terms of complex networks.