Authors
Feng Xue, Xiaoxiu Tan, Chenhong Zhang, Hongyu Zhao, Tao Wang
Published in
Microbiome. Volume 14. Issue 1. Aug 19, 2026. Epub Aug 19, 2026.
Abstract
Understanding the ecological mechanisms of host-associated microbial ecosystems typically relies on either cross-sectional or time-series data. Cross-sectional analyses are limited in their ability to assess intervention effects, whereas time-series models require dense and informative sampling that is often impractical.
Here, we present an enhanced Neural Ordinary Differential Equations (NeuralODE) framework that, for the first time, integrates cross-sectional data into the dynamic modeling of sparse and weakly informative temporal data. We develop two instantiations of this framework, tailored to relative and absolute abundances, and introduce a dynamic keystoneness metric to quantify species importance over time. Across simulated and real-data benchmarks, incorporating cross-sectional data improved performance over competing methods, particularly in data-scarce settings. Moreover, biological validation demonstrated that the framework recovers experimentally supported interactions and prioritizes identified influential species.
Together, these results establish our method as a reliable framework for mechanistic modeling of microbial ecosystems, offering new insights into their dynamic behavior. Video Abstract.
PMID:
42661217
Bibliographic data and abstract were imported from PubMed on 28 Aug 2026.
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