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Influential nodes in influence maximization problems are of great importance for the spread of information in complex networks. In this study, we identify influential nodes, called influential genes, in protein–protein interaction (PPI) networks. In theory, information can percolate through an entire network when influential genes are activated. We propose a new framework by taking the asymmetry of influence into account to identify genes that are more influential in PPI networks. In the framework, we identify influential genes by considering the heterogeneity of influence. As such, the minimal set of influential genes in the influence maximization problem can be mapped onto the optimal set of genes in the optimal percolation problem. We identify the influential genes in the PPI networks of five species, and the results show that the genes identified by our method are more influential and tend to be located in the core of a PPI network. In addition, we find that influential genes tend to be more significantly enriched in essential yeast genes, tumor suppressor genes, and drug target genes. [ABSTRACT FROM AUTHOR]

Abstract: Studying protein complexes is very important in biological processes because it helps reveal the structure–functionality relationships in a protein complex. Much attention has been paid to accurately predicting the protein complexes from the increasing amount of protein–protein interaction (PPI) data. Almost all of the current algorithms that concern the detection of protein complexes focus on discovering dense subgraphs based on the observation that dense subgraphs in a biological network may correspond to protein complexes. However, such an assumption would throw away further topological information about complexes. In this paper introducing the core-attachment concept, a novel core-attachment algorithm is developed by detecting the cores and attachments, respectively. To detect the cores of protein complexes, a virtual network is constructed using the eigenvalues and eigenvectors of the network involved, where each maximal clique corresponds to a protein complex core. With this notion, the problem of detecting protein complex cores is transformed into the classic all-cliques problem. The attachments are then merged into their cores to yield biologically meaningful complexes. A comprehensive comparison between MCL, DPClus, Coach, DECAFF and our algorithm has been made by comparing the predicted protein complexes with the benchmarked complexes. Experimental results indicate that our algorithm outperforms the MCL, DPClus and DECAFF and has comparative performance with the Coach algorithm in terms of the accuracy of prediction. Moreover, the detected complexes with core-attachment structures match well with the benchmark data, demonstrating that our algorithm can provide more insightful perspectives. Robustness analysis further shows that the algorithm is very robust. [Copyright &y& Elsevier]