2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)
Dec. 15, 2016 to Dec. 18, 2016
Le Ou-Yang , College of Information Engineering, Shenzhen University, 518060, China
Hong Yan , Department of Electronic and Engineering, City University of Hong Kong, Hong Kong
Xiao-Fei Zhang , School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan 430079, China
The detection of protein complexes from protein-protein interaction (PPI) networks is an important step toward understanding the functional organization within cells. A great number of graph clustering algorithms have been proposed to undertake this task. Since PPI data collected by high-throughput technologies is quite noisy, simply applying graph clustering algorithms on PPI data is generally not adequate to achieve reliable prediction results. Behind protein interactions, there are protein domains that interact with each other. Jointly exploiting protein-protein interactions and domain-domain interactions (DDI) have the potential to increase the accuracy of protein complex detection. However, traditional graph clustering algorithms focus on clustering proteins within a single PPI network, and cannot make use of information inherent in other heterogeneous networks. In this paper, we proposed a novel generative model to perform multi-network clustering. Unlike previous protein complex detection algorithms that can only utilize the information within a single PPI network, our model is a flexible framework that can take into account PPIs, DDIs and domain-protein associations to achieve more consistent and reliable clustering results. Experiment results on real data demonstrate that our method performs much better than state-of-the-art protein complex detection techniques.
Proteins, Clustering algorithms, Protein engineering, Mathematical model, Prediction algorithms, Linear programming, Heterogeneous networks
L. Ou-Yang, Hong Yan and X. Zhang, "Identifying protein complexes via multi-network clustering," 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China, 2016, pp. 645-650.