TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata.

ABSTRACT:

Background

The gut microbiome plays a crucial role in human health, making it a cornerstone of modern biomedical research. To study its structure and dynamics, machine learning models are increasingly used to identify key microbial patterns associated with disease and environmental factors, but their performance is often limited by the intrinsic complexity of microbiome data and the small size of available cohorts. In this context, data augmentation has emerged as a promising strategy to overcome these challenges by generating artificial microbiome profiles.

Results

We introduce TaxaPLN, a data augmentation method based on PLN-Tree generative models, which leverages the taxonomy and a data-driven sampler to generate realistic synthetic microbiome compositions. Additionally,

SUBMITTER: Chaussard A 

PROVIDER: S-EPMC12763835 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature