Chinese Science Bulletin

, Volume 52, Issue 21, pp 2938–2944 | Cite as

Comparing the biological coherence of network clusters identified by different detection algorithms

Articles Bioinformatics

Abstract

Protein-protein interaction networks serve to carry out basic molecular activity in the cell. Detecting the modular structures from the protein-protein interaction network is important for understanding the organization, function and dynamics of a biological system. In order to identify functional neighborhoods based on network topology, many network cluster identification algorithms have been developed. However, each algorithm might dissect a network from a different aspect and may provide different insight on the network partition. In order to objectively evaluate the performance of four commonly used cluster detection algorithms: molecular complex detection (MCODE), NetworkBlast, shortest-distance clustering (SDC) and Girvan-Newman (G-N) algorithm, we compared the biological coherence of the network clusters found by these algorithms through a uniform evaluation framework. Each algorithm was utilized to find network clusters in two different protein-protein interaction networks with various parameters. Comparison of the resulting network clusters indicates that clusters found by MCODE and SDC are of higher biological coherence than those by NetworkBlast and G-N algorithm.

Keywords

network cluster detection algorithms biological relevance function entropy protein-protein interaction network 

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Copyright information

© Science in China Press 2007

Authors and Affiliations

  1. 1.Graduate School, College of Life SciencesBeijing Normal UniversityBeijingChina
  2. 2.Chinese Academy of Science Key Laboratory for Molecular Developmental Biology, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina

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