Showing results (1-10 of 159) with videos related to
Sort By:
Pageof 16
Arxiv|June 17, 2024
Distributional bias compromises leave-one-out cross-validationGeorge I Austin, Itsik Pe'er, Tal KoremScience Advances|November 28, 2025
Distributional bias compromises leave-one-out cross-validationGeorge I Austin, Itsik Pe'er, Tal KoremBiorxiv : the Preprint Server for Biology|January 20, 2025
Compositional transformations can reasonably introduce phenotype-associated values into sparse featuresGeorge I Austin, Tal KoremThe Journal of Infectious Diseases|August 27, 2024
Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis PerspectiveGeorge I Austin, Tal KoremMsystems|May 2, 2025
Compositional transformations can reasonably introduce phenotype-associated values into sparse featuresGeorge I Austin, Tal KoremNature Microbiology|March 28, 2025
Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction modelsGeorge I Austin, Aya Brown Kav, Shahd ElNaggar, et al.Genome Research|January 6, 2022
Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth ratesTyler A Joseph, Philippe Chlenski, Aviya Litman, et al.Nature Biotechnology|March 17, 2023
Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome dataGeorge I Austin, Heekuk Park, Yoli Meydan, et al.Biorxiv : the Preprint Server for Biology|February 26, 2024
Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction modelsGeorge I Austin, Aya Brown Kav, Heekuk Park, et al.Biorxiv : the Preprint Server for Biology|September 18, 2025
Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic ProfilesJulia Urban, Aya Brown Kav, William F Kindschuh, et al.Pageof 16