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Xiaoxiu Tan

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Open access Aug 2026

Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE

An enhanced Neural Ordinary Differential Equations (NeuralODE) framework is presented that, for the first time, integrates cross-sectional data into the dynamic modeling of sparse and weakly informative temporal data and introduces a dynamic keystoneness metric to quantify species importance over time.

Feng Xue, Xiaoxiu Tan, Chenhong Zhang et al. · 0 citations
Open access Jul 2026

gNODE: gLV model-informed neural ordinary differential equations for modeling microbial community dynamics

The gNODE framework is proposed, a framework that integrates the generalized Lotka-Volterra model with neural ordinary differential equations (NeuralODEs) to jointly predict microbial community dynamics, infer species interactions, and quantify the functional contributions of key taxa.

Xiaoxiu Tan, Feng Xue, Lu Xie et al. · 0 citations

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