Multivariable association discovery in population-scale meta-omics studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely...

Full description

Saved in:

Bibliographic Details
Published in:	PLoS computational biology Vol. 17; no. 11; p. e1009442
Main Authors:	Mallick, Himel, Rahnavard, Ali, McIver, Lauren J., Ma, Siyuan, Zhang, Yancong, Nguyen, Long H., Tickle, Timothy L., Weingart, George, Ren, Boyu, Schwager, Emma H., Chatterjee, Suvo, Thompson, Kelsey N., Wilkinson, Jeremy E., Subramanian, Ayshwarya, Lu, Yiren, Waldron, Levi, Paulson, Joseph N., Franzosa, Eric A., Bravo, Hector Corrada, Huttenhower, Curtis
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 16.11.2021 Public Library of Science (PLoS)
Subjects:	Analysis Biology and Life Sciences Computational Biology Computer and Information Sciences Computer Simulation Engineering and Technology Epidemiology Gastrointestinal Microbiome Humans Inflammatory Bowel Diseases - genetics Inflammatory Bowel Diseases - metabolism Inflammatory Bowel Diseases - pathology Medicine and Health Sciences Metabolomics Methods Multivariate Analysis Observational studies Physical Sciences Research and Analysis Methods United States
ISSN:	1553-7358, 1553-734X, 1553-7358
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!