A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.

ABSTRACT: Longitudinal microbiome data provide a unique opportunity to explore dynamic interactions between microbial communities and disease progression. However, these data are often characterized by missing values, sparse signals, and limited interpretability, which impede effective biomarker discovery and accurate disease modeling. Therefore, we propose SysLM, a comprehensive deep learning framework for systematic analysis of longitudinal microbiome data. It comprises two synergistic modules: SysLM-I and SysLM-C. SysLM-I focuses on the task of missing-value inference, combines metadata and three feature enhancement strategies, and comprehensively captures temporal causality and long-term dependence through Temporal Convolutional Network and Bi-directional Long Short-Term Memory modules. SysLM-C

SUBMITTER: Wang L 

PROVIDER: S-EPMC12636166 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature