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- W2891415103 abstract "Abstract Acquisition of time-series data supported by the recent popularization of genomic sequencing has revealed dynamic nature of a microbial community. Recent advances in analyzing community dynamics of microbial species cover not only applying existing traditional methods but also developing new methods. This chapter introduces traditional and novel mathematical and statistical approaches to analyze community dynamics of microbial species. In the former part, we introduce data fitting methods to link time-series data of community dynamics to mechanistic modeling. While data-driven approaches in the later part specifically introduce traditional and new methods to analyze time-series data of community dynamics based on reconstruction of attractors." @default.
- W2891415103 created "2018-09-27" @default.
- W2891415103 creator A5042347115 @default.
- W2891415103 date "2018-01-01" @default.
- W2891415103 modified "2023-09-26" @default.
- W2891415103 title "Data-Driven Mathematical Modeling of Microbial Community Dynamics" @default.
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- W2891415103 doi "https://doi.org/10.1016/bs.host.2018.08.001" @default.
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