MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
about
Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations.A phylogenetic transform enhances analysis of compositional microbiota dataPhylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets.Reduced microbial diversity in adult survivors of childhood acute lymphoblastic leukemia and microbial associations with increased immune activation.mixOmics: An R package for 'omics feature selection and multiple data integration.Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders.Enhancing the Resolution of Rumen Microbial Classification from Metatranscriptomic Data Using Kraken and Mothur.Bacterial community assembly from cow teat skin to ripened cheeses is influenced by grazing systems.A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America.Environmental fluctuations accelerate molecular evolution of thermal tolerance in a marine diatom.Gut microbiota components are associated with fixed airway obstruction in asthmatic patients living in the tropics.A Natural History of Actinic Keratosis and Cutaneous Squamous Cell Carcinoma MicrobiomesInfluence of cigarette smoking on the human duodenal mucosa-associated microbiotaOil type and temperature dependent biodegradation dynamics - Combining chemical and microbial community data through multivariate analysis
P2860
Q31166515-D2466C4C-784F-4721-807A-D335FED6038BQ36280857-A8F6944A-BA14-4B84-94BF-75627E335A83Q36306938-0AE96917-D6D8-4E68-A57F-FE2DA4016624Q37713517-AE1048B1-643C-457C-AE4F-C550D8286A97Q45846807-13BADEE6-A2D4-4487-B901-24AD994DDD19Q46251992-9EBB5248-5FC7-4734-8008-4B2B4768C304Q47140369-951D5627-AFAE-4A38-A76A-816D7F54CAFFQ48276527-C5774707-5E7D-4E16-974A-3239BC4E5F21Q52355341-4BC766F8-C8EB-4E60-9961-5774B4059EFAQ55188640-C083A2CD-A554-4D1A-9FBC-9CD36AB87B6EQ55428575-F86A9EDB-92A2-41FE-B844-A67329F9DCD8Q57291160-703CF1D3-631D-4038-954A-BF2D9945D77BQ58694782-8B834993-D186-4676-B43C-B5FA7C113D15Q58797611-D9927B87-0E3E-496D-A0BD-02EA85B78E9A
P2860
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@ast
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@en
type
label
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@ast
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@en
prefLabel
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@ast
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@en
P2093
P2860
P1433
P1476
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
@en
P2093
François Bartolo
Kim-Anh Lê Cao
Mary-Ellen Costello
Pascale Rondeau
Rémi Brazeilles
Vanessa Anne Lakis
Xin-Yi Chua
P2860
P304
P356
10.1371/JOURNAL.PONE.0160169
P407
P577
2016-08-11T00:00:00Z