A graph-theoretic method for mining overlapping functional modules in protein interaction networks
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Clustering PPI data based on Improved functional-flow model through Quantum-behaved PSO
International Journal of Data Mining and Bioinformatics
A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Recent computational analyses of protein interaction networks have attempted to understand cellular organizations, processes and functions. Various topology-based clustering methods have been applied to the protein interaction networks. However, they have been in difficulties due to unreliable interaction data and the specific features of the networks such as small-world and scale-free properties. In this paper, we present an information flow-based approach for analyzing the weighted protein interaction networks, which are integrated with other biological knowledge. Our approach is designed to identify overlapping functional modules. The algorithm selects a small number of informative proteins based on the weighted connectivity, and simulates the information flow through the network from each informative protein. Our experimental results show that the modules generated by our algorithm correspond to real functional associations of proteins. Furthermore, we demonstrate that our approach outperforms other previous methods in terms of accuracy.