Open AccessA novel network aligner for the analysis of multiple protein-protein interaction networks
Author(s) -
Jing Chen,
Jia Huang
Publication year - 2021
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis200909030c
Subject(s) - computer science , cluster analysis , similarity (geometry) , sequence (biology) , data mining , interaction network , network topology , theoretical computer science , topology (electrical circuits) , artificial intelligence , image (mathematics) , mathematics , computer network , genetics , combinatorics , biology , biochemistry , chemistry , gene
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.