MicroSTNet: a spatio-temporal graph-based framework for time-series microbiome analysis.

ABSTRACT: The structure and function of microbial communities are profoundly influenced by spatio-temporal dynamics. While existing machine learning algorithms are extensively used for phenotype prediction based on microbial communities, particularly for disease forecasting, they fail to fully utilize the spatio-temporal dynamics embedded in microbial data. Moreover, data collected at a single time point often proves inadequate for the accurate prediction of host or environmental phenotypes. This study investigates the interaction dynamics of microbial communities in closed environments using data from two independent research projects. We introduce the microbial spatio-temporal network model, which combines two-stream spatio-temporal graph convolutional networks with long short-term memory to predi

SUBMITTER: Gao S 

PROVIDER: S-EPMC12546993 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature