Jean‐Daniel Zucker

CurateMake: an auditable workflow for multi-source ITS reference database harmonisation and phylogenetic validation

CurateMake is a workflow for harmonizing and validating ITS reference databases, improving taxonomic coherence and error correction through nomenclatural standardization and phylogenetic validation.

MetagenBERT: a Transformer Architecture using Foundational DNA Read Embedding Models to enhance Disease Classification

MetagenBERT uses Transformer models to embed DNA reads for disease classification, achieving comparable performance to existing methods for cirrhosis and better results for type 2 diabetes.

Deep Learning Methods in Metagenomics: a systematic review

This paper reviews deep learning methods in metagenomics, focusing on their application to human gut microbiome analysis and their potential to improve disease prediction and patient stratification.

Towards end-to-end disease prediction from raw metagenomic data

This paper introduces metagenome2vec, an end-to-end deep learning approach that classifies diseases from raw metagenomic reads, achieving performance comparable to conventional pipelines.

Interpretable and accurate prediction models for metagenomics data

The paper develops interpretable metagenomics prediction models based on microbial taxon abundance ratios, additions, and subtractions, achieving performance comparable to or better than traditional machine learning methods.