Quantifying uncertainty in microbiome-based prediction using Gaussian processes with microbial community dissimilarities.

ABSTRACT:

Summary

The human microbiome is closely associated with the health and disease of the human host. Machine learning models have recently utilized the human microbiome to predict health conditions and disease status. Quantifying predictive uncertainty is essential for the reliable application of these microbiome-based prediction models in clinical settings. However, uncertainty quantification in such prediction models remains unexplored. In this study, we have developed a probabilistic prediction model using a Gaussian process (GP) with a kernel function that incorporates microbial community dissimilarities. We evaluated the performance of probabilistic prediction across three regression tasks: chronological age, body mass index, and disease severity, using publicly available human g

SUBMITTER: Adachi A 

PROVIDER: S-EPMC11919817 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature