Differential biclustering for gene expression analysis
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Fast and accurate estimation of shortest paths in large graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Identifying the genes that change between two conditions, such as normal versus cancer, is a crucial task in understanding the causes of diseases. Differential networking has emerged as a powerful approach to achieve this task and to detect the changes in the corresponding network structures. The goal of differential networking is to identify the differentially connected genes between two networks. However, the current differential networking methods primarily depend on pair-wise comparisons of the genes based on their degrees in the two networks. Therefore, these methods cannot capture all the topological changes in the network structure. In this paper, we propose a novel differential networking algorithm, DiffRank, to rank the genes based on their contribution to the differences between two gene co-expression networks. To achieve this goal, we define two novel scoring measures: a local structure measure, differential connectivity, and a global structure measure, differential betweenness centrality. These measures are combined within a PageRank-style framework and optimized by propagating them through the network. Finally, the genes are ranked based on the their propagated scores. We demonstrate the effectiveness of DiffRank on several gene expression datasets, and we show that our method provides biologically interesting rankings.