HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

Abstract

Cancer drivers play an important role in regulating cell growth, cell cycles, and DNA replication. Identifying these cancer drivers provides cancer researchers with indispensable knowledge that has important implications for clinical decision making. Some methods have been recently proposed to identify coding and non-coding cancer drivers through controllability analysis in network and eigenvector centrality based on community detection. However, the performance of these methods is not satisfactory. In this work, we focus on the strategy of selecting a set of critical nodes in cancer-special network as cancer drivers, and propose a novel approach for identifying coding and non-coding drives via a network-based voting mechanism. We name our approach HWVoteRank. Compared with two recent methods to identify cancer drivers, CBNA and NIBNA, and three algorithms for identifying key nodes on BRCA dataset, our method can achieve the best efficiency. By analyzing the results, it is found that our approach has better ability in identifying miRNA cancer drivers. We also applied our approach to identification of drivers of miRNA during Epithelial–Mesenchymal transition and drivers for cancer subtype. Through literature research, we found that those drivers explored by our approach are of biological significance.

The average trapping time of non-nearest-neighbor jumps on nested networks

“…Complex networks have attracted the attention of scholars in the fields of computer technology [1,2], biological information [3][4][5][6], physical science [7][8][9][10], social relations [11,12] and so on. Complex networks describe realworld networks as mathematical models.…”

“…As an important research object in the random walk process, the trapping problem [20] describes the physical phenomenon that wandering particles on the network are absorbed by the trap node. This problem is widely used in the capture of attackers on the Internet [1], the repair of paralyzed sites in the transportation network [21], the optimization of signal transmission in the communication network [2], the prediction and prevention of virus transmission [4,5] and so on. As an indicator of trap efficiency, the average trapping time (ATT) represents the dynamic exploration of a random walk on a network containing trap nodes, meaning the average sum of the time taken by all nodes in the network to reach the trap node.…”

“…The walking style of these problems considers a simple random walk, that is, moving from a node to its nearest neighbor (NN) node. However, in real problems, particle diffusion may produce jumping behavior, that is, moving from a node to its non-nearest-neighbor (NNN) node, such as effective path selection in social traffic, extension of the effective path of wireless devices to the current, stability of the ferromagnetic ground state of organic ferromagnetic materials, surface diffusion of physical metals [29][30][31], preventing the spread and metastasis of cancer cells [4], etc. Wu et al studied the non-neighbor wandering trap problem on a class of 3-level SG networks [32], and obtained the analytic expression of the corresponding index.…”