Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks.

ABSTRACT: Sequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli, and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-pred

SUBMITTER: McGeachie MJ 

PROVIDER: S-EPMC4745046 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature