Dynamic interaction network inference from longitudinal microbiome data.

ABSTRACT: BACKGROUND:Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data. RESULTS:Here, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between t

SUBMITTER: Lugo-Martinez J 

PROVIDER: S-EPMC6446388 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature