125 related articles for article (PubMed ID: 37985956)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. A compositional mediation model for a binary outcome: Application to microbiome studies.
Sohn MB; Lu J; Li H
Bioinformatics; 2021 Dec; 38(1):16-21. PubMed ID: 34415327
[TBL] [Abstract][Full Text] [Related]
7. Analyzing the overall effects of the microbiome abundance data with a Bayesian predictive value approach.
Zhang X; Yi N
Stat Methods Med Res; 2022 Oct; 31(10):1992-2003. PubMed ID: 35695247
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. Generalized linear models with linear constraints for microbiome compositional data.
Lu J; Shi P; Li H
Biometrics; 2019 Mar; 75(1):235-244. PubMed ID: 30039859
[TBL] [Abstract][Full Text] [Related]
12. Generalized Hotelling's test for paired compositional data with application to human microbiome studies.
Zhao N; Zhan X; Guthrie KA; Mitchell CM; Larson J
Genet Epidemiol; 2018 Jul; 42(5):459-469. PubMed ID: 29737047
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. A Bayesian method for detecting pairwise associations in compositional data.
Schwager E; Mallick H; Ventz S; Huttenhower C
PLoS Comput Biol; 2017 Nov; 13(11):e1005852. PubMed ID: 29140991
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. An adaptive direction-assisted test for microbiome compositional data.
Zhang W; Liu A; Zhang Z; Chen G; Li Q
Bioinformatics; 2022 Jul; 38(14):3493-3500. PubMed ID: 35640978
[TBL] [Abstract][Full Text] [Related]
18. Compositional zero-inflated network estimation for microbiome data.
Ha MJ; Kim J; Galloway-Peña J; Do KA; Peterson CB
BMC Bioinformatics; 2020 Dec; 21(Suppl 21):581. PubMed ID: 33371887
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. gmcoda: Graphical model for multiple compositional vectors in microbiome studies.
Fang H
Bioinformatics; 2023 Nov; 39(11):. PubMed ID: 37975866
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
