SAMNA: accurate alignment of multiple biological networks based on simulated annealing

Author:

Chen Jing12,Wang Zixiang1,Huang Jia1

Affiliation:

1. School of Artificial Intelligence and Computer Science, Jiangnan University , Wuxi , China

2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University , Wuxi , China

Abstract

Abstract Proteins are important parts of the biological structures and encode a lot of biological information. Protein–protein interaction network alignment is a model for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims at finding the mapping relationship among multiple network nodes, so as to transfer the knowledge across species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge. This paper proposes a new global network alignment algorithm called Simulated Annealing Multiple Network Alignment (SAMNA), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm. Finally, the SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance.

Funder

Jiangsu Province Science Foundation for Youths of China

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

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