@george-austin.bsky.social
Reposted
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 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.
November 28, 2025 at 7:32 PM
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 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.
Reposted
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/...
Thread explaining the key points below.
journals.asm.org/doi/10.1128/...
May 2, 2025 at 1:59 PM
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/...
Thread explaining the key points below.
journals.asm.org/doi/10.1128/...
Reposted
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...
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...
Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models - Nature Microbiology
DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates robust analyses that generali...
www.nature.com
March 27, 2025 at 4:01 PM
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...
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...