A Novel Network Aligner for the Analysis of Multiple Protein-protein Interaction Networks

Jing Chen^{1, 2} and Jia Huang¹

School of Artificial Intelligence and Computer Science
Jiangnan University Wuxi, China
chenjing@jiangnan.edu.cn
Jiangsu Provincial Engineering Laboratory of Pattern
Recognition and Computing Intelligence, Jiangnan University, Wuxi, China

Abstract

The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the nodes is calculated according to the network structures. Second, the seed-and-extend framework is used to conduct an iterative search. In each iteration, a clustering method is combined to generate the alignment. Extensive experimental results show that ACCMNA outperformed the state-of-the-art algorithms in producing functionally consistent and topological conservation alignments within an acceptable running time.

Key words

graph data analysis, big data, protein-protein interaction network, network clustering, seed-and-extend strategy

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200909030C

Publication information

Volume 18, Issue 4 (September 2021)
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Chen, J., Huang, J.: A Novel Network Aligner for the Analysis of Multiple Protein-protein Interaction Networks. Computer Science and Information Systems, Vol. 18, No. 4, 1427–1444. (2021), https://doi.org/10.2298/CSIS200909030C