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 *

188 related articles for article (PubMed ID: 35867822)

  • 1. LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.
    Hu Y; Satten GA; Hu YJ
    Proc Natl Acad Sci U S A; 2022 Jul; 119(30):e2122788119. PubMed ID: 35867822
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Impact of Experimental Bias on Compositional Analysis of Microbiome Data.
    Hu Y; Satten GA; Hu YJ
    Genes (Basel); 2023 Sep; 14(9):. PubMed ID: 37761917
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Impact of experimental bias on compositional analysis of microbiome data.
    Hu Y; Satten GA; Hu YJ
    bioRxiv; 2023 Feb; ():. PubMed ID: 36798370
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Analysis of composition of microbiomes: a novel method for studying microbial composition.
    Mandal S; Van Treuren W; White RA; Eggesbø M; Knight R; Peddada SD
    Microb Ecol Health Dis; 2015; 26():27663. PubMed ID: 26028277
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency.
    Yue Y; Read TD; Fedirko V; Satten GA; Hu YJ
    Res Sq; 2023 Oct; ():. PubMed ID: 37886529
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis.
    Fernandes AD; Reid JN; Macklaim JM; McMurrough TA; Edgell DR; Gloor GB
    Microbiome; 2014; 2():15. PubMed ID: 24910773
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. mbDecoda: a debiased approach to compositional data analysis for microbiome surveys.
    Zong Y; Zhao H; Wang T
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38701410
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions.
    Yang L; Chen J
    Microbiome; 2022 Aug; 10(1):130. PubMed ID: 35986393
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Analysis and correction of compositional bias in sparse sequencing count data.
    Kumar MS; Slud EV; Okrah K; Hicks SC; Hannenhalli S; Corrada Bravo H
    BMC Genomics; 2018 Nov; 19(1):799. PubMed ID: 30400812
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Scalable estimation and regularization for the logistic normal multinomial model.
    Zhang J; Lin W
    Biometrics; 2019 Dec; 75(4):1098-1108. PubMed ID: 31009062
    [TBL] [Abstract][Full Text] [Related]  

  • 14. An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates.
    Alsup A; Nissen E; Salas LA; Molinaro AM; Reiner A; Liu S; Madsen TE; Liu L; Auer PL; Christensen BC; Wiencke JK; Kelsey KT; Koestler DC
    Epigenomics; 2024; 16(15-16):1067-1080. PubMed ID: 39093129
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Compositional data analysis of the microbiome: fundamentals, tools, and challenges.
    Tsilimigras MC; Fodor AA
    Ann Epidemiol; 2016 May; 26(5):330-5. PubMed ID: 27255738
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Approximation of a Microbiome Composition Shift by a Change in a Single Balance Between Two Groups of Taxa.
    Odintsova VE; Klimenko NS; Tyakht AV
    mSystems; 2022 Jun; 7(3):e0015522. PubMed ID: 35532211
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Normalization and microbial differential abundance strategies depend upon data characteristics.
    Weiss S; Xu ZZ; Peddada S; Amir A; Bittinger K; Gonzalez A; Lozupone C; Zaneveld JR; Vázquez-Baeza Y; Birmingham A; Hyde ER; Knight R
    Microbiome; 2017 Mar; 5(1):27. PubMed ID: 28253908
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ.
    Epp Schmidt D; Maul JE; Yarwood SA
    Microb Ecol; 2023 Nov; 86(4):2790-2801. PubMed ID: 37563275
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel normalization and differential abundance test framework for microbiome data.
    Ma Y; Luo Y; Jiang H
    Bioinformatics; 2020 Jul; 36(13):3959-3965. PubMed ID: 32311021
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.