BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations.

Rahman, Gibraan; Morton, James T; Martino, Cameron; Sepich-Poore, Gregory D; Allaband, Celeste; Guccione, Caitlin; Chen, Yang; Hakim, Daniel; Estaki, Mehrbod; Knight, Rob

Rahman, Gibraan; Morton, James T; Martino, Cameron; Sepich-Poore, Gregory D; Allaband, Celeste; Guccione, Caitlin; Chen, Yang; Hakim, Daniel; Estaki, Mehrbod; Knight, Rob.

Afiliación

Rahman G; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Morton JT; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
Martino C; Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Sepich-Poore GD; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Allaband C; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
Guccione C; Micronoma, San Diego, CA, USA.
Chen Y; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Hakim D; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Estaki M; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
Knight R; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA.

bioRxiv ; 2023 Feb 02.

Article en En | MEDLINE | ID: mdl-36778470

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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