Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
ACM SIGKDD Explorations Newsletter
International Journal of Data Mining and Bioinformatics
Protein Interaction Networks: Computational Analysis
Protein Interaction Networks: Computational Analysis
On evolutionary spectral clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
flowNet: Flow-Based Approach for Efficient Analysis of Complex Biological Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Functional Flow Simulation Based Analysis of Protein Interaction Network
BIBE '10 Proceedings of the 2010 IEEE International Conference on Bioinformatics and Bioengineering
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Functional module detection in Protein-Protein Interaction (PPI) networks is essential to understanding the organization, evolution and interaction of the cellular systems. In recent years, most of the researches have focused on detecting the functional modules from the static PPI networks. However, sometimes the structure of the PPI networks changes in response to stimuli resulting in the changes of both the composition and functionality of these modules. These changes occur gradually and can be thought of as an evolution of the functional modules. In our opinions the evolutionary analysis of functional modules is a key to form important insights of the functional modules' underlying behaviors, particularly when targeting complex living systems. In this paper, we propose a novel computational framework which integrates a PPI network with multiple dynamic gene coexpression networks to categorize and track the evolutionary pattern of functional modules over consecutive time-stamps. We first propose a method to construct dynamic PPI networks, and then design a new functional influence based algorithm to detect the functional modules from these dynamic PPI networks. Based on the results of this approach, we provide a simple but effective method to characterize and track the evolutionary patterns of dynamic modules, which involves detecting evolutionary events between modules found at consecutive timestamps. Extensive experiments on the fermentation process dataset of S. cerevisiae show that the proposed framework not only outperforms previous functional module detection methods, but also efficiently tracks the evolutionary patterns of functional modules.