scholarly journals Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

2021 ◽  
Author(s):  
Shion Hosoda ◽  
Tsukasa Fukunaga ◽  
Michiaki Hamada
Keyword(s):  
Gut Microbiota ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Continuous Time ◽  
Hidden Markov ◽  
Hidden Variables ◽  
Microbial Interactions ◽  
Interaction Networks ◽  
Time Varying ◽  
Microbial Interaction

AbstractMotivationAccumulating 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.ResultsIn 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.AvailabilityThe C++ and python source codes of the Umibato software are available at http://github.com/shion-h/[email protected], [email protected]

A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance

2018 ◽  
Vol 18 (3) ◽  
pp. 853-868 ◽  
Author(s):  
Shenfang Yuan ◽  
Jinjin Zhang ◽  
Jian Chen ◽  
Lei Qiu ◽  
Weibo Yang
Keyword(s):  
Fatigue Crack ◽  
Markov Model ◽  
Crack Propagation ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Guided Wave ◽  
Time Varying ◽  
Damage Evaluation ◽  
Model Based

During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the performance improvement of hidden Markov model itself. In this article, the training stability problem when constructing the guided wave–hidden Markov model initialized by usually adopted k-means clustering method and its influence to damage evaluation were researched first by applying it to fatigue crack propagation evaluation of an attachment lug. After illustrating the problem of stability induced by k-means clustering, a novel uniform initialization Gaussian mixture model–based guided wave–hidden Markov model was proposed that provides steady and reliable construction of the guided wave–hidden Markov model. The advantage of the proposed method is demonstrated by lug fatigue crack propagation evaluation experiments.

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