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Tsinghua Science and Technology 2013, 18(5): 454-461 https://doi.org/10.1109/TST.2013.6616519
Open Access | Issue | Published: 03 October 2013

Community Detection in Disease-Gene Network Based on Principal Component Analysis

Show Author's Information Wei Liu( ),  Ling Chen
Department of Computer Science, Information Science and Technology College, Yangzhou University, Yangzhou 225127, China
Keywords:
disease-gene network, principal component analysis, community detection
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Liu W, Chen L. Community Detection in Disease-Gene Network Based on Principal Component Analysis. Tsinghua Science and Technology, 2013, 18(5): 454-461. https://doi.org/10.1109/TST.2013.6616519
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Abstract Full text About this article

The identification of communities is imperative in the understanding of network structures and functions. Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.


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About this article

Community Detection in Disease-Gene Network Based on Principal Component Analysis

Show Author's information Wei Liu( ), Ling Chen
Department of Computer Science, Information Science and Technology College, Yangzhou University, Yangzhou 225127, China

Abstract

The identification of communities is imperative in the understanding of network structures and functions. Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.

Keywords: disease-gene network, principal component analysis, community detection

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Publication history

Received: 09 August 2013
Revised: 23 August 2013
Accepted: 26 August 2013
Published: 03 October 2013
Issue date: October 2013

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© The author(s) 2013

Acknowledgements

This research was supported in part by the Natural Science Foundation of Education Department of Jiangsu Province (No. 12KJB520019), the National Science Foundation of Jiangsu Province (No. BK20130452), Science and Technology Innovation Foundation of Yangzhou University (No. 2012CXJ026), the National Natural Science Foundation of China (Nos. 61070047, 61070133, and 61003180) and the National Key Basic Research and Development (973) Program of China (No. 2012CB316003).

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