Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Functional annotation of regulatory pathways
Bioinformatics
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Graph sparsification by effective resistances
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Protein function prediction based on patterns in biological networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Role of centrality in network-based prioritization of disease genes
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Learning protein functions from bi-relational graph of proteins and function annotations
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Detecting disease genes based on semi-supervised learning and protein-protein interaction networks
Artificial Intelligence in Medicine
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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In recent years, many algorithms have been developed to narrow down the set of candidate disease genes implicated by genome wide association studies (GWAS), using knowledge on protein-protein interactions (PPIs). All of these algorithms are based on a common principle; functional association between proteins is correlated with their connectivity/proximity in the PPI network. However, recent research also reveals that networks are organized into recurrent network schemes that underlie the mechanisms of cooperation among proteins with different function, as well as the crosstalk between different cellular processes. In this paper, we hypothesize that proteins that are associated with similar diseases may exhibit patterns of "topological similarity" in PPI networks. Motivated by these observations, we introduce the notion of "topological profile", which represents the location of a protein in the network with respect to other proteins. Based on this notion, we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to develop algorithms that prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. Systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance (OMIM) database show that the proposed algorithm, VAVIEN, clearly outperforms state-of-the-art network based prioritization algorithms. VAVIEN is available as a web service at http://www.diseasegenes.org.