Out after peer-review: www.science.org/doi/full/10.... Our bottom line stayed: never use leave-one-out cross-validation as it has inherent train-test leakage. Consider our Rebalanced version instead! We now also account for regression and nested cross-validation, with more extensive benchmarking.

Our paper explaining why Gihawi et al. failed to prove an error in the normalization used by the 2020 cancer #microbiome analysis now out as a Matters Arising in @asm.org #mSystems (w/ @george-austin.bsky.social) 🖥️ 🧬 Thread explaining the key points below. journals.asm.org/doi/10.1128/...

Happy to share DEBIAS-M, our new method for domain adaptation and bias correction in #microbiome data.🧬🖥️ Microbiome data is very variable, with substantial study- and batch-effects. DEBIAS-M corrects these, enabling robust and generalizable analyses. A quick thread: www.nature.com/articles/s41...