Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model | Zendy

Shion Hosoda | Zendy; Tsukasa Fukunaga | Zendy; Michiaki Hamada | Zendy

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

Author(s) -

Shion Hosoda,

Tsukasa Fukunaga,

Michiaki Hamada

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab287

Subject(s) - computer science , python (programming language) , inference , hidden markov model , bayesian network , machine learning , regression , bayesian probability , artificial intelligence , regression analysis , data mining , conditional dependence , statistics , mathematics , operating system

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.

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