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Weighted Consensus Clustering for Identifying Functional Modules in Protein-Protein Interaction Networks

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4 Author(s)
Yi Zhang ; Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USA ; Erliang Zeng ; Tao Li ; Narasimhan, G.

In this article we present a new approach - weighted consensus clustering to identify the clusters in Protein-protein interaction (PPI) networks where each cluster corresponds to a group of functionally similar proteins. In weighed consensus clustering, different input clustering results weigh differently, i.e., a weight for each input clustering is introduced and the weights are automatically determined by an optimization process. We evaluate our proposed method with standard measures such as modularity, normalized mutual information (NMI) and the Gene Ontology (GO) consortium database and compare the performance of our approach with other consensus clustering methods. Experimental results demonstrate the effectiveness of our proposed approach.

Published in:

Machine Learning and Applications, 2009. ICMLA '09. International Conference on

Date of Conference:

13-15 Dec. 2009

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