Paper Info
Title
Meta-network: optimized species-species network analysis for microbial communities.
Abstract
The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need. Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all samples) as well as association rule mining (mining more complex forms of correlations like indirect correlation and mutual information), this framework outperforms other methods in restoring the microbial communities, based on two cohorts of microbial communities: (a) the loose definition strategy is capable to generate more reasonable relationships among species in the species-species co-occurrence network; (b) important species-species co-occurrence patterns could not be identified by other existing approaches, but could successfully generated by association rule mining. Results have shown that the species-species co-occurrence network we generated are much more informative than those based on traditional methods. Meta-Network has consistently constructed more meaningful networks with biologically important clusters, hubs, and provides a general approach towards deciphering the species-species co-occurrence networks.
Year
DOI
Venue
2019
10.1186/s12864-019-5471-1
BMC Genomics
Keywords
DocType
Volume
Microbial network, Data-mining, Network analysis, Associate-rule mining
Journal
2
Issue
ISSN
Citations 
suppl
1471-2164
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Pengshuo Yang100.34
Shaojun Yu200.34
Lin Cheng300.34
Kang Ning4144.19