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...

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Vydané v:	PLoS computational biology Ročník 17; číslo 11; s. e1009442
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 16.11.2021 Public Library of Science (PLoS)
Predmet:	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
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