185 related articles for article (PubMed ID: 32686846)
1. Bayesian compositional regression with structured priors for microbiome feature selection.
Zhang L; Shi Y; Jenq RR; Do KA; Peterson CB
Biometrics; 2021 Sep; 77(3):824-838. PubMed ID: 32686846
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Bayesian compositional regression with microbiome features via variational inference.
Scott DAV; Benavente E; Libiseller-Egger J; Fedorov D; Phelan J; Ilina E; Tikhonova P; Kudryavstev A; Galeeva J; Clark T; Lewin A
BMC Bioinformatics; 2023 May; 24(1):210. PubMed ID: 37217852
[TBL] [Abstract][Full Text] [Related]
4. A Bayesian zero-inflated Dirichlet-multinomial regression model for multivariate compositional count data.
Koslovsky MD
Biometrics; 2023 Dec; 79(4):3239-3251. PubMed ID: 36896642
[TBL] [Abstract][Full Text] [Related]
5. Bayesian compositional models for ordinal response.
Zhang L; Zhang X; Leach JM; Rahman AF; Yi N
Stat Methods Med Res; 2024 Jun; 33(6):1043-1054. PubMed ID: 38654396
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. An empirical Bayes approach to normalization and differential abundance testing for microbiome data.
Liu T; Zhao H; Wang T
BMC Bioinformatics; 2020 Jun; 21(1):225. PubMed ID: 32493208
[TBL] [Abstract][Full Text] [Related]
8. Transformation and differential abundance analysis of microbiome data incorporating phylogeny.
Zhou C; Zhao H; Wang T
Bioinformatics; 2021 Dec; 37(24):4652-4660. PubMed ID: 34302462
[TBL] [Abstract][Full Text] [Related]
9. A Bayesian joint model for compositional mediation effect selection in microbiome data.
Fu J; Koslovsky MD; Neophytou AM; Vannucci M
Stat Med; 2023 Jul; 42(17):2999-3015. PubMed ID: 37173609
[TBL] [Abstract][Full Text] [Related]
10. Comparing Analytical Methods for the Gut Microbiome and Aging: Gut Microbial Communities and Body Weight in the Osteoporotic Fractures in Men (MrOS) Study.
Shardell M; Parimi N; Langsetmo L; Tanaka T; Jiang L; Orwoll E; Shikany JM; Kado DM; Cawthon PM
J Gerontol A Biol Sci Med Sci; 2020 Jun; 75(7):1267-1275. PubMed ID: 32025711
[TBL] [Abstract][Full Text] [Related]
11. Opportunities and limits of combining microbiome and genome data for complex trait prediction.
Pérez-Enciso M; Zingaretti LM; Ramayo-Caldas Y; de Los Campos G
Genet Sel Evol; 2021 Aug; 53(1):65. PubMed ID: 34362312
[TBL] [Abstract][Full Text] [Related]
12. A distance based multisample test for high-dimensional compositional data with applications to the human microbiome.
Zhang Q; Dao T
BMC Bioinformatics; 2020 Dec; 21(Suppl 9):205. PubMed ID: 33272203
[TBL] [Abstract][Full Text] [Related]
13. Multivariate log-contrast regression with sub-compositional predictors: Testing the association between preterm infants' gut microbiome and neurobehavioral outcomes.
Liu X; Cong X; Li G; Maas K; Chen K
Stat Med; 2022 Feb; 41(3):580-594. PubMed ID: 34897772
[TBL] [Abstract][Full Text] [Related]
14. Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.
Coull BA; Bobb JF; Wellenius GA; Kioumourtzoglou MA; Mittleman MA; Koutrakis P; Godleski JJ
Res Rep Health Eff Inst; 2015 Jun; (183 Pt 1-2):5-50. PubMed ID: 26333238
[TBL] [Abstract][Full Text] [Related]
15. Bayesian variable selection for multivariate zero-inflated models: Application to microbiome count data.
Lee KH; Coull BA; Moscicki AB; Paster BJ; Starr JR
Biostatistics; 2020 Jul; 21(3):499-517. PubMed ID: 30590511
[TBL] [Abstract][Full Text] [Related]
16. A logistic normal multinomial regression model for microbiome compositional data analysis.
Xia F; Chen J; Fung WK; Li H
Biometrics; 2013 Dec; 69(4):1053-63. PubMed ID: 24128059
[TBL] [Abstract][Full Text] [Related]
17. A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.
Jiang S; Xiao G; Koh AY; Kim J; Li Q; Zhan X
Biostatistics; 2021 Jul; 22(3):522-540. PubMed ID: 31844880
[TBL] [Abstract][Full Text] [Related]
18. Compositional knockoff filter for high-dimensional regression analysis of microbiome data.
Srinivasan A; Xue L; Zhan X
Biometrics; 2021 Sep; 77(3):984-995. PubMed ID: 32683674
[TBL] [Abstract][Full Text] [Related]
19. Stochastic variational variable selection for high-dimensional microbiome data.
Dang T; Kumaishi K; Usui E; Kobori S; Sato T; Toda Y; Yamasaki Y; Tsujimoto H; Ichihashi Y; Iwata H
Microbiome; 2022 Dec; 10(1):236. PubMed ID: 36566203
[TBL] [Abstract][Full Text] [Related]
20. Sparse least trimmed squares regression with compositional covariates for high-dimensional data.
Monti GS; Filzmoser P
Bioinformatics; 2021 Nov; 37(21):3805-3814. PubMed ID: 34358286
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]