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
PDF (497.5 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

PBNA: An Improved Probabilistic Biological Network Alignment Method

School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 210096, China.
Division of Math and Computer Science, University of South Carolina Upstate, Spartanburg, SC 29303, USA,
Show Author Information

Abstract

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem. However, it is worth noting that interactions in biological networks, like many other processes in the biological realm, are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment (PBNA) is proposed. Based on IsoRank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function (PGF) to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction (PPI) networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than IsoRank does in most of the cases based on the Gene Ontology Consistency (GOC) measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.

References

[1]
R. Singh, J. Xu, and B. Berger, Global alignment of multiple protein interaction networks with application to functional orthology detection, PNAS, vol. 105, no. 35, pp. 12763-12768, 2008.
[2]
X. L. Guo, L. Gao, and X. Chen, Models and algorithms for alignment of biological networks, (in Chinese), Journal of Software, vol. 21, no. 9, pp. 2089-2106, 2010.
[3]
H. Ogata, W. Fujibuchi, and S. Goto, A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters, Nucleic Acids Research, vol. 28, no. 20, pp. 4021-4028, 2000.
[4]
E. Hirsh and R. Sharan, Identification of conserved protein complexes based on a model of protein network evolution, Bioinformatics, vol. 23, no 2, pp. e170-e176, 2007.
[5]
R. Singh, J. Xu, and B. Berger, Pairwise global alignment of protein interaction networks by matching neighborhood topology, in Research in Computational Molecular Biology. Springer Berlin Heidelberg, 2007, pp. 16-31.
[6]
M. Narayanan and R. M. Karp, Comparing protein interaction networks via a graph match-and-split algorithm, Journal of Computational Biology, vol. 14, no. 7, pp. 892-907, 2007.
[7]
A. Todor, A. Dobra, and T. Kahveci, Probabilistic biological network alignment, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 109-121, 2013.
[8]
Z. Liang, M. Xu, M. Teng, and L. Niu, Comparison of protein interaction networks reveals species conservation and divergence, BMC Bioinformatics, vol. 7, no. 1, pp. 457-472, 2006.
[9]
A. Chatr-Aryamontri, A. Ceol, L. M. Montecchi, G. Nardelli, M. Schenider, L. Castagnoli, and G. Cesareni, MINT: The molecular interaction database, Nucleic Acids Research, vol. 35, no. s1, pp. 572-574, 2007.
[10]
M. Koyuturk, Y. Kim, U. Topkara, S. Subramaniam, W. Szpankowski, and A. Grama, Pairwise alignment of protein interaction networks, Journal of Computational Biology, vol. 13, no. 2, pp. 182-199, 2006.
[11]
M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, and H. Butler, Gene ontology: Tool for the unification of biology, Nat. Genet., vol. 25, no. 1, pp. 25-29, 2000.
[12]
W. Tian and N. F. Samatova, Pairwise alignment of interaction networks by fast identification of maximal conserved patterns, Pacific Symposium on Biocomputing, vol. 14, pp. 99-110, 2009.
[13]
E. Almaas, Biological impacts and context of network theory, The Journal of Experimental Biology, vol. 210, pp. 1548-1558, 2007.
[14]
G. W. Klau, A new graph-based method for pairwise global network alignment, BMC Bioinformatics, vol 10, pp. 59-67, 2009.
[15]
F. Towfic, M. H. W. Greenlee, and V. Honavar, Aligning biomolecular networks using modular graph kernels, in Algorithms in Bioinformatics. Springer Berlin Heidelberg, 2009, pp. 345-361.
[16]
W. Guan, J. Wang, and F. He, The advance in research methods for large-scale protein-protein interactions (in Chinese), Chinese Bulletin of Life Sciences, vol. 18, no. 5, pp. 507-512, 2006.
[17]
E. Silva and M. P. H. Stumpf, Complex networks and simple models in biology, Journal of The Royal Society Interface, vol. 2, no. 5, pp. 419-430, 2005.
[18]
J. Sun, J. Xu, Y. Li, and T. Shi, Analysis and application of large-scale protein-protein interaction data, (in Chinese), Chinese Science Bulletin, vol. 50, no. 19, pp. 2055-2060, 2005.
[19]
P. Jancura, J. Heringa, and E. Marchiori, Divide, align and full-search for discovering conserved protein complexes, in Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2008, pp. 71-82.
[20]
A. E. Aladag and C. Erten, SPINAL: Scalable protein interaction network alignment, Bioinformatics, vol. 29, no. 7, pp. 917-924, 2013.
[21]
H. W. Kuhn, The Hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol. 2, nos. 1-2, pp. 83-97, 1955.
[22]
M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, The KEGG resource for deciphering the genome, Nucleic Acids Research, vol. 32, no. s1, pp. D277-D280, 2004.
Tsinghua Science and Technology
Pages 658-667
Cite this article:
Zhao M, Zhong W, He J. PBNA: An Improved Probabilistic Biological Network Alignment Method. Tsinghua Science and Technology, 2014, 19(6): 658-667. https://doi.org/10.1109/TST.2014.6961034

627

Views

18

Downloads

0

Crossref

N/A

Web of Science

0

Scopus

0

CSCD

Altmetrics

Received: 16 June 2014
Accepted: 23 June 2014
Published: 20 November 2014
The Author(s)
Return