A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

about

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

Item

http://www.wikidata.org/entity/Q55322320

description

2018 nî lūn-bûn

@nan

2018年の論文

@ja

2018年学术文章

@wuu

2018年学术文章

@zh

2018年学术文章

@zh-cn

2018年学术文章

@zh-hans

2018年学术文章

@zh-my

2018年学术文章

@zh-sg

2018年學術文章

@yue

2018年學術文章

@zh-hant

name

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@en

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@nl

type

Item

label

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@en

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@nl

prefLabel

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@en

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@nl

P1476

A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

@en

P2093

Juhee Lee

http://www.w3.org/2001/XMLSchema#string

Marilou Sison-Mangus

http://www.w3.org/2001/XMLSchema#string

P2860

Differential expression analysis for sequence count data

A core gut microbiome in obese and lean twins

Waste not, want not: why rarefying microbiome data is inadmissible

A two-part mixed-effects model for analyzing longitudinal microbiome compositional data.

Negative binomial mixed models for analyzing microbiome count data

Moderated statistical tests for assessing differences in tag abundance.

Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories.

Two dynamic regimes in the human gut microbiome

Phytoplankton-Associated Bacterial Community Composition and Succession during Toxic Diatom Bloom and Non-Bloom Events

The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women.

P304

522

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.3389/FMICB.2018.00522

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P577

2018-03-26T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29632519

http://www.w3.org/2001/XMLSchema#string

P921

microbiome

P932

5879107

http://www.w3.org/2001/XMLSchema#string