These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
403 related articles for article (PubMed ID: 27187200)
61. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Sharma D; Xu W Bioinformatics; 2021 Nov; 37(21):3707-3714. PubMed ID: 34213529 [TBL] [Abstract][Full Text] [Related]
62. Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model. Tipton L; Cuenco KT; Huang L; Greenblatt RM; Kleerup E; Sciurba F; Duncan SR; Donahoe MP; Morris A; Ghedin E BioData Min; 2018; 11():12. PubMed ID: 29983746 [TBL] [Abstract][Full Text] [Related]
63. pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis. Plantinga AM; Chen J; Jenq RR; Wu MC Bioinformatics; 2019 Oct; 35(19):3567-3575. PubMed ID: 30863868 [TBL] [Abstract][Full Text] [Related]
64. A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome Studies. Koh H; Li Y; Zhan X; Chen J; Zhao N Front Genet; 2019; 10():458. PubMed ID: 31156711 [TBL] [Abstract][Full Text] [Related]
65. The mixed model for the analysis of a repeated-measurement multivariate count data. Martin I; Uh HW; Supali T; Mitreva M; Houwing-Duistermaat JJ Stat Med; 2019 May; 38(12):2248-2268. PubMed ID: 30761571 [TBL] [Abstract][Full Text] [Related]
66. mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis. Zeng Y; Li J; Wei C; Zhao H; Wang T Genome Biol; 2022 Apr; 23(1):94. PubMed ID: 35422001 [TBL] [Abstract][Full Text] [Related]
67. Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis. Fang R; Wagner BD; Harris JK; Fillon SA Epidemiol Infect; 2016 Aug; 144(11):2447-55. PubMed ID: 27049299 [TBL] [Abstract][Full Text] [Related]
68. Bayesian compositional generalized linear models for analyzing microbiome data. Zhang L; Zhang X; Yi N Stat Med; 2024 Jan; 43(1):141-155. PubMed ID: 37985956 [TBL] [Abstract][Full Text] [Related]
69. A Zero-inflated Beta-binomial Model for Microbiome Data Analysis. Hu T; Gallins P; Zhou YH Stat (Int Stat Inst); 2018; 7(1):. PubMed ID: 30197785 [TBL] [Abstract][Full Text] [Related]
70. Efficient and Accurate Inference of Mixed Microbial Population Trajectories from Longitudinal Count Data. Joseph TA; Pasarkar AP; Pe'er I Cell Syst; 2020 Jun; 10(6):463-469.e6. PubMed ID: 32684275 [TBL] [Abstract][Full Text] [Related]
72. Testing hypotheses about the microbiome using the linear decomposition model (LDM). Hu YJ; Satten GA Bioinformatics; 2020 Aug; 36(14):4106-4115. PubMed ID: 32315393 [TBL] [Abstract][Full Text] [Related]
73. gCoda: Conditional Dependence Network Inference for Compositional Data. Fang H; Huang C; Zhao H; Deng M J Comput Biol; 2017 Jul; 24(7):699-708. PubMed ID: 28489411 [TBL] [Abstract][Full Text] [Related]
74. Quantitative microbiome profiling links gut community variation to microbial load. Vandeputte D; Kathagen G; D'hoe K; Vieira-Silva S; Valles-Colomer M; Sabino J; Wang J; Tito RY; De Commer L; Darzi Y; Vermeire S; Falony G; Raes J Nature; 2017 Nov; 551(7681):507-511. PubMed ID: 29143816 [TBL] [Abstract][Full Text] [Related]
75. Predicting microbiomes through a deep latent space. García-Jiménez B; Muñoz J; Cabello S; Medina J; Wilkinson MD Bioinformatics; 2021 Jun; 37(10):1444-1451. PubMed ID: 33289510 [TBL] [Abstract][Full Text] [Related]
76. Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients. Galloway-Peña JR; Smith DP; Sahasrabhojane P; Wadsworth WD; Fellman BM; Ajami NJ; Shpall EJ; Daver N; Guindani M; Petrosino JF; Kontoyiannis DP; Shelburne SA Genome Med; 2017 Feb; 9(1):21. PubMed ID: 28245856 [TBL] [Abstract][Full Text] [Related]
77. An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data. Wadsworth WD; Argiento R; Guindani M; Galloway-Pena J; Shelburne SA; Vannucci M BMC Bioinformatics; 2017 Feb; 18(1):94. PubMed ID: 28178947 [TBL] [Abstract][Full Text] [Related]
78. Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data. Dousti Mousavi N; Yang J; Aldirawi H Genes (Basel); 2023 Feb; 14(2):. PubMed ID: 36833330 [TBL] [Abstract][Full Text] [Related]
79. Analyzing differences between microbiome communities using mixture distributions. Shestopaloff K; Escobar MD; Xu W Stat Med; 2018 Nov; 37(27):4036-4053. PubMed ID: 30039541 [TBL] [Abstract][Full Text] [Related]
80. PERFect: PERmutation Filtering test for microbiome data. Smirnova E; Huzurbazar S; Jafari F Biostatistics; 2019 Oct; 20(4):615-631. PubMed ID: 29917060 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]