Graph classes: a survey
Graph Drawing Software
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Efficient algorithms for detecting signaling pathways in protein interaction networks
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Smart Miner: a new framework for mining large scale web usage data
Proceedings of the 18th international conference on World wide web
Mobility profiler: A framework for discovering mobility profiles of cell phone users
Pervasive and Mobile Computing
Discovering better navigation sequences for the session construction problem
Data & Knowledge Engineering
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High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein-protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. Unfortunately, many computational methods are sensitive to noise in protein interaction networks; and therefore they are not able to make biologically accurate inferences. In this article, we propose a novel technique based on integration of topological measures for removing non-specific interactions in a large-scale protein-protein interaction network. After transforming a given protein interaction network using line graph transformation, we compute clustering coefficient and betweenness centrality measures for all the edges in the network. Motivated by the modular organization of specific protein interactions in a cell, we remove edges with low clustering coefficient and high betweenness centrality values. We also utilize confidence estimates that are provided by probabilistic interaction prediction techniques. We validate our proposed method by comparing the results of a molecular complex detection algorithm (MCODE) to a ground truth set of known Saccharomyces cerevisiae complexes in the MIPS complex catalogue database. Our results show that, by removing false-positive interactions in the S. cerevisiae network, we can significantly increase the biological accuracy of the complexes reported by MCODE.