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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

121 related articles for article (PubMed ID: 37930883)

  • 1. Compositional analysis of microbiome data using the linear decomposition model (LDM).
    Hu YJ; Satten GA
    Bioinformatics; 2023 Nov; 39(11):. PubMed ID: 37930883
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Compositional analysis of microbiome data using the linear decomposition model (LDM).
    Hu YJ; Satten GA
    bioRxiv; 2023 May; ():. PubMed ID: 37398068
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A new approach to testing mediation of the microbiome at both the community and individual taxon levels.
    Yue Y; Hu YJ
    Bioinformatics; 2022 Jun; 38(12):3173-3180. PubMed ID: 35512399
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM.
    Zhu Z; Satten GA; Hu YJ
    Bioinformatics; 2022 May; 38(10):2915-2917. PubMed ID: 35561163
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. A rarefaction-based extension of the LDM for testing presence-absence associations in the microbiome.
    Hu YJ; Lane A; Satten GA
    Bioinformatics; 2021 Jul; 37(12):1652-1657. PubMed ID: 33479757
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Testing microbiome associations with survival times at both the community and individual taxon levels.
    Hu Y; Li Y; Satten GA; Hu YJ
    PLoS Comput Biol; 2022 Sep; 18(9):e1010509. PubMed ID: 36103548
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome.
    Hu YJ; Satten GA
    Bioinformatics; 2022 Aug; 38(15):3689-3697. PubMed ID: 35723568
    [TBL] [Abstract][Full Text] [Related]  

  • 9. LinDA: linear models for differential abundance analysis of microbiome compositional data.
    Zhou H; He K; Chen J; Zhang X
    Genome Biol; 2022 Apr; 23(1):95. PubMed ID: 35421994
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data.
    Zhu Z; Satten GA; Mitchell C; Hu YJ
    Microbiome; 2021 Jun; 9(1):133. PubMed ID: 34108046
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data.
    Wang C; Hu J; Blaser MJ; Li H
    Bioinformatics; 2020 Jan; 36(2):347-355. PubMed ID: 31329243
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies.
    Calle ML; Pujolassos M; Susin A
    BMC Bioinformatics; 2023 Mar; 24(1):82. PubMed ID: 36879227
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories.
    Banjac J; Sprenger N; Dogra SK
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36469345
    [TBL] [Abstract][Full Text] [Related]  

  • 19.
    Fink I; Abdill RJ; Blekhman R; Grieneisen L
    mSystems; 2022 Jun; 7(3):e0138021. PubMed ID: 35499306
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
    Kodalci L; Thas O
    PLoS One; 2023; 18(9):e0292055. PubMed ID: 37751452
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.