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 *

104 related articles for article (PubMed ID: 37589589)

  • 1. Adjusting for gene-specific covariates to improve RNA-seq analysis.
    Jeon H; Lim KS; Nguyen Y; Nettleton D
    Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37589589
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Bon-EV: an improved multiple testing procedure for controlling false discovery rates.
    Li D; Xie Z; Zand M; Fogg T; Dye T
    BMC Bioinformatics; 2017 Jan; 18(1):1. PubMed ID: 28049414
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm.
    Biswas A; Chattopadhyay G
    Biom J; 2024 Mar; 66(2):e2200204. PubMed ID: 38356198
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
    Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Power and type I error rate of false discovery rate approaches in genome-wide association studies.
    Yang Q; Cui J; Chazaro I; Cupples LA; Demissie S
    BMC Genet; 2005 Dec; 6 Suppl 1(Suppl 1):S134. PubMed ID: 16451593
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Accurate error control in high-dimensional association testing using conditional false discovery rates.
    Liley J; Wallace C
    Biom J; 2021 Jun; 63(5):1096-1130. PubMed ID: 33682201
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.
    Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA
    Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Wavelet thresholding with bayesian false discovery rate control.
    Tadesse MG; Ibrahim JG; Vannucci M; Gentleman R
    Biometrics; 2005 Mar; 61(1):25-35. PubMed ID: 15737075
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Simulation, power evaluation and sample size recommendation for single-cell RNA-seq.
    Su K; Wu Z; Wu H
    Bioinformatics; 2020 Dec; 36(19):4860-4868. PubMed ID: 32614380
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Resampling-based empirical Bayes multiple testing procedures for controlling generalized tail probability and expected value error rates: focus on the false discovery rate and simulation study.
    Dudoit S; Gilbert HN; van der Laan MJ
    Biom J; 2008 Oct; 50(5):716-44. PubMed ID: 18932138
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures.
    Chen X; Doerge RW; Heyse JF
    Biom J; 2018 Jul; 60(4):761-779. PubMed ID: 29748972
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A direct approach to estimating false discovery rates conditional on covariates.
    Boca SM; Leek JT
    PeerJ; 2018; 6():e6035. PubMed ID: 30581661
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A comparative study on the unified model based multifactor dimensionality reduction methods for identifying gene-gene interactions associated with the survival phenotype.
    Lee JW; Lee S
    BioData Min; 2021 Mar; 14(1):17. PubMed ID: 33648540
    [TBL] [Abstract][Full Text] [Related]  

  • 14. RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts.
    Graubert A; Aguet F; Ravi A; Ardlie KG; Getz G
    Bioinformatics; 2021 Sep; 37(18):3048-3050. PubMed ID: 33677499
    [TBL] [Abstract][Full Text] [Related]  

  • 15. SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition.
    Dietrich A; Sturm G; Merotto L; Marini F; Finotello F; List M
    Bioinformatics; 2022 Sep; 38(Suppl_2):ii141-ii147. PubMed ID: 36124800
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A novel method for single-cell data imputation using subspace regression.
    Tran D; Tran B; Nguyen H; Nguyen T
    Sci Rep; 2022 Feb; 12(1):2697. PubMed ID: 35177662
    [TBL] [Abstract][Full Text] [Related]  

  • 17. FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.
    Zhang Y; Sun H; Mandava A; Aevermann BD; Kollmann TR; Scheuermann RH; Qiu X; Qian Y
    Bioinformatics; 2022 Oct; 38(20):4735-4744. PubMed ID: 36018232
    [TBL] [Abstract][Full Text] [Related]  

  • 18. scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data.
    Yin Q; Wang Y; Guan J; Ji G
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34913057
    [TBL] [Abstract][Full Text] [Related]  

  • 19. V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.
    Lawlor N; Marquez EJ; Lee D; Ucar D
    Bioinformatics; 2020 Jun; 36(11):3582-3584. PubMed ID: 32119082
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Best linear inverse probability weighted estimation for two-phase designs and missing covariate regression.
    Wang CY; Dai J
    Stat Med; 2019 Jul; 38(15):2783-2796. PubMed ID: 30908669
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.