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PFC: An Efficient Soft Graph Clustering Method for PPI Networks Based on Purifying and Filtering the Coupling Matrix

  • Ying LiuEmail author
  • Amir Foroushani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

One of the most pressing problems of the post genomic era is identifying protein functions. Clustering Protein-Protein-Interaction networks is a systems biological approach to this problem. Traditional Graph Clustering Methods are crisp, and allow only membership of each node in at most one cluster. However, most real world networks contain overlapping clusters. Recently the need for scalable, accurate and efficient overlapping graph clustering methods has been recognized and various soft (overlapping) graph clustering methods have been proposed. In this paper, an efficient, novel, and fast overlapping clustering method is proposed based on purifying and filtering the coupling matrix (PFC). PFC is tested on PPI networks. The experimental results show that PFC method outperforms many existing methods by a few orders of magnitude in terms of average statistical (hypergeometrical) confidence regarding biological enrichment of the identified clusters.

Keywords

Protein-protein interaction networks Purifying and filtering the coupling matrix Overlapping clusters Functional modules 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Division of Computer Science, Mathematics, and Science, College of Professional StudiesSt. John’s UniversityQueensUSA
  2. 2.Department of Computer ScienceTexas State UniversitySan MarcosUSA

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