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

Alexandre Chaussard1, Anna Bonnet2, Sylvain Le Corff2

  • 1Laboratoire de Probabilités, Statistique et Modélisation, LPSM, Sorbonne Université, Université Paris Cité, CNRS, F-75005, Paris, France. alexandre.chaussard@sorbonne-universite.fr.

BMC Bioinformatics
|November 29, 2025
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Summary

TaxaPLN enhances microbiome data analysis by generating realistic synthetic data, improving machine learning model performance. This method preserves ecological properties and integrates host metadata for better predictions.

Keywords:
Data augmentationGenerative modelMicrobiologyVariational inference

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Area of Science:

  • Microbiome Research
  • Computational Biology
  • Machine Learning

Background:

  • The gut microbiome is vital for human health and a key area in biomedical research.
  • Machine learning models analyze microbiome data but face challenges due to data complexity and small cohort sizes.
  • Data augmentation offers a solution by creating artificial microbiome profiles.

Purpose of the Study:

  • To introduce TaxaPLN, a novel data augmentation method for microbiome datasets.
  • To develop a conditional extension for covariate-aware microbiome data generation.
  • To evaluate the performance and ecological validity of TaxaPLN compared to existing methods.

Main Methods:

  • Developed TaxaPLN using PLN-Tree generative models and a data-driven sampler.
  • Implemented a conditional extension utilizing feature-wise linear modulation for metadata integration.
  • Tested TaxaPLN on diverse microbiome datasets, assessing ecological properties and predictive performance.

Main Results:

  • TaxaPLN generates realistic synthetic microbiome compositions while preserving ecological properties.
  • The method generally improves or maintains predictive performance across various tasks.
  • The conditional TaxaPLN variant sets a new benchmark for metadata-aware microbiome augmentation, outperforming baselines.

Conclusions:

  • TaxaPLN offers a model-based framework for augmenting microbiome data, maintaining ecological and clinical relevance.
  • Integration of taxonomic structure and host metadata enhances predictive modeling capabilities.
  • TaxaPLN is available as an open-source Python package (plntree) for reproducible research.