The myth of open data, reproducibility, responsibility, and accountability in science, and your role in it

Leave-one-out cross-validation, a common machine learning evaluation method, has a pernicious flaw; a practical fix is presented.

Leave-one-out cross-validation, a common machine learning evaluation method, has a pernicious flaw; a practical fix is presented.

We apply systems biology approaches to decipher the metabolic interactions between the microbiome and its human host in diverse clinical settings, aiming towards personalized microbiome-based therapeu...

Nature Microbiology - In this Consensus Statement, the authors outline strategies for processing, analysing and interpreting low-biomass microbiome samples, and provide recommendations to minimize...

Statistical normalizations are used in differential analyses to address sample-to-sample variation in sequencing depth. Yet normalizations make strong, implicit assumptions about the scale of biologic...

Microbiologist Michael Baym studies antibiotic resistance at Harvard Medical School. He lost millions in federal funding this week.

Gihawi et al. claim that finding that a transformation turned highly sparse (mostly zero) features into features that are associated with a phenotype is sufficient to conclude that there is informatio...

Gihawi et al. claim that finding that a transformation turned highly sparse (mostly zero) features into features that are associated with a phenotype is sufficient to conclude that there is informatio...

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...

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...