Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module. (December 2020)

• Record Type: Journal Article
• Authors: Yu, Limin; Shen, Xianjun; Yang, Jincai; Wei, Kaiping; Zhong, Duo; Xiang, Ruilong
• Is Part Of: Evolutionary bioinformatics online. Volume 16(2020)
• Publication Date: 2020-12
• Subjects: Microbial higher-order module -- game-theory -- hypergraph clustering -- evolutionary stability strategy; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934320970572
• Languages: English
• ISSNs: 1176-9343

Abstract

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded fromhttps://github.com/ylm0505/HCGI .