期刊
BIOINFORMATICS
卷 37, 期 -, 页码 I16-I24出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab287
关键词
-
类别
资金
- JSPS/MEXT KAKENHI [JP19J20117, JP20H05582, JP20H00624, JP19H01152, JP18KT0016]
This study developed a novel microbial interaction inference method Umibato that can estimate time-varying microbial interaction networks and outperformed existing methods. Umibato showed superior performance on synthetic datasets and a mouse gut microbiota dataset in providing new insights into the relationship between consumed diets and gut microbiota.
Motivation: Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. Results: In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.
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