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A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease
Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological...
Autores principales: | Luna, Pamela N., Mansbach, Jonathan M., Shaw, Chad A. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769610/ https://www.ncbi.nlm.nih.gov/pubmed/33315858 http://dx.doi.org/10.1371/journal.pcbi.1008473 |
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