New PreMiEr Publication: Simpler Methods Hold Up in Microbiome Machine Learning Analysis
A new PreMiEr meta-analysis finds that complex beta diversity transformations offer no consistent advantage over simpler methods in microbiome machine learning, giving researchers more flexibility in their analytical choices.
A new meta-analysis from PreMiEr researchers finds that more sophisticated microbiome analysis methods do not consistently outperform simpler, well-established ones in machine learning applications. The study, published in a 2026 peer-reviewed journal, tested 107 features across five microbiome datasets, comparing a range of beta diversity approaches that vary in their accounting for phylogeny, compositionality, and data sparsity. Results showed that method complexity alone was not a reliable predictor of better classification performance, and that commonly used pseudo-F scores were a poor proxy for machine learning accuracy. The findings give analysts flexibility to choose methods based on their dataset rather than chasing incremental gains from newer approaches.
Read more here: https://doi.org/10.64898/2026.01.20.699043