AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

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

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

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 networkprincipal component analysiscommunity detection

References

[1]
L. A.Adamic, D.Wilkinson, B. A.Huberman, and E.Adar, A literature based method for identifying gene-disease connections, in Proceedings of the IEEE Computer Society Conference on Bioinformatics, Stanford, CA, USA, 2002, pp. 109-117.
[2]
H.Al-Mubaidand R. K.Singh, A new text mining approach for finding protein to disease associations, American Journal of Biochemistry and Biotechnology, vol. 1, no. 3, pp. 145-152, 2005.
Crossref Google Scholar
[3]
K.Goh, M. E.Cusick, D.Valle, B.Childs, M.Vidal, and A.-L.Barabási, The human disease network, Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 21, pp. 8685-8690, 2007.
Crossref
[4]
S.Fortunato, Community detection in Graphs, Physics Reports, vol. 486, pp. 75-174, 2010. .
Crossref Google Scholar
[5]
M.Fiedler, Algebraic connectivity of graphs, Czechoslovak Mathematical Journal, vol. 23, no. 98, pp. 298-305, 1973.
Crossref Google Scholar
[6]
B. W.Kernighanand S.Lin, An efficient heuristic procedure for partitioning graphs, Bell Systems Technical Journal, vol. 49, no. 2, pp. 291-307, 1970.
Crossref Google Scholar
[7]
M. E. J.Newmanand M.Girvan, Finding and evaluating community structure in networks, Phys. Rev. E, vol. 69, 2004. .
Crossref Google Scholar
[8]
M. E. J.Newman, Fast algorithm for detecting community structure in networks, Phys. Rev. E, vol. 69, p. 066133, 2004. .
Crossref Google Scholar
[9]
A.Clauset, M. E. J.Newman, and C.Moore, Finding community structure in very large networks, Phys. Rev. E, vol. 70, p. 066111, 2004. .
Crossref Google Scholar
[10]
B.Xiang, E. H.Chen, and T.Zhou, Finding community structure based on subgraph similarity, Studies in Computational Intelligence, 2009. .
Crossref Google Scholar
[11]
J. H.Ruanand W. X.Zhang, Identifying network communities with a high resolution, Phys. Rev. E, vol. 77, no. 1, 2008. .
Crossref Google Scholar
[12]
J.Duchand A.Arenas, Community detection in complex networks using extremal optimization, Phys. Rev. E, vol. 72, p. 027104, 2005. .
Crossref Google Scholar
[13]
X. T.Wang, G. R.Chen, and H. T.Lu, A very fast algorithm for detecting community structures in complex networks, Physica A, vol. 384, pp. 667-674, 2007. .
Crossref Google Scholar
[14]
M. E. J.Newman, Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, 2006. .
Crossref Google Scholar
[15]
D. B.Chen, Y.Fu, and M. S.Shang, A fast and efficient heuristic algorithm for detecting community structures in complex networks, Physica A, vol. 388, no. 13, pp. 2741-2749, 2009. .
Crossref Google Scholar
[16]
W.Chen, J.Lu, and J.Liang, Research in disease- gene network based on bipartite network projection, (in Chinese), Complex Systems and Complexity Science, vol. 6, no. 1, 2009. .
Crossref Google Scholar
Tsinghua Science and Technology
Volume 18 Issue 5,
October 2013
Pages 454-461
DOI: 10.1109/TST.2013.6616519
Cite this article:
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

668

Views

23

Downloads

12

Crossref

N/A

Web of Science

14

Scopus

0

CSCD

Altmetrics
Received: 09 August 2013
Revised: 23 August 2013
Accepted: 26 August 2013
Published: 03 October 2013
© The author(s) 2013
Return