Latent Space Temporal Model of Microbial Abundance to Predict Domination and Bacteremia

2018/08/31 by Ruiqi Zhong, Zhong, Ruiqi, Tyler Joseph +5
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Quantitative Methods (q-bio.QM) #q-bio.QM

paper · pdf · doi:10.48550/arxiv.1808.10795

Experiment code available at https://github.com/ZhongRuiqi1997/NIPS2017MLCB, software at https://github.com/ZhongRuiqi1997/Kalman-Filter-Intestinal-Microbiota

arxiv created 2018/08/31 · arxiv updated 2018/09/03

Abstract

Gut microbial composition has been linked to multiple health outcomes. Yet, temporal analysis of this composition had been limited to deterministic models. In this paper, we introduce a probabilistic model for the dynamics of intestinal microbiomes that takes into account interaction among bacteria as well as external effects such as antibiotics. The model successfully deals with pragmatic issues such as random measurement error and varying time intervals between measurements through latent space modeling. We demonstrate utility of the model by using latent state features to predict the clinical events of intestinal domination and bacteremia, improving accuracy over existing methods. We further leverage this framework to validate known links between antibiotics and clinical outcomes, while discovering new ones.

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