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 *

134 related articles for article (PubMed ID: 26160885)

  • 1. Count ratio model reveals bias affecting NGS fold changes.
    Erhard F; Zimmer R
    Nucleic Acids Res; 2015 Nov; 43(20):e136. PubMed ID: 26160885
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.
    Li X; Brock GN; Rouchka EC; Cooper NGF; Wu D; O'Toole TE; Gill RS; Eteleeb AM; O'Brien L; Rai SN
    PLoS One; 2017; 12(5):e0176185. PubMed ID: 28459823
    [TBL] [Abstract][Full Text] [Related]  

  • 3. aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.
    Yang W; Rosenstiel P; Schulenburg H
    BMC Genomics; 2019 May; 20(1):364. PubMed ID: 31077153
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Normalization and noise reduction for single cell RNA-seq experiments.
    Ding B; Zheng L; Zhu Y; Li N; Jia H; Ai R; Wildberg A; Wang W
    Bioinformatics; 2015 Jul; 31(13):2225-7. PubMed ID: 25717193
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Differential gene expression analysis using coexpression and RNA-Seq data.
    Yang EW; Girke T; Jiang T
    Bioinformatics; 2013 Sep; 29(17):2153-61. PubMed ID: 23793751
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates.
    Gu X
    Brief Bioinform; 2016 Mar; 17(2):243-8. PubMed ID: 26108230
    [TBL] [Abstract][Full Text] [Related]  

  • 7. PLNseq: a multivariate Poisson lognormal distribution for high-throughput matched RNA-sequencing read count data.
    Zhang H; Xu J; Jiang N; Hu X; Luo Z
    Stat Med; 2015 Apr; 34(9):1577-89. PubMed ID: 25641202
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data.
    Yoon S; Nam D
    BMC Genomics; 2017 May; 18(1):408. PubMed ID: 28545404
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identifying differentially expressed transcripts from RNA-seq data with biological variation.
    Glaus P; Honkela A; Rattray M
    Bioinformatics; 2012 Jul; 28(13):1721-8. PubMed ID: 22563066
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SimSeq: a nonparametric approach to simulation of RNA-sequence datasets.
    Benidt S; Nettleton D
    Bioinformatics; 2015 Jul; 31(13):2131-40. PubMed ID: 25725090
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.
    Lin Y; Golovnina K; Chen ZX; Lee HN; Negron YL; Sultana H; Oliver B; Harbison ST
    BMC Genomics; 2016 Jan; 17():28. PubMed ID: 26732976
    [TBL] [Abstract][Full Text] [Related]  

  • 12. BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data.
    Gu J; Wang X; Halakivi-Clarke L; Clarke R; Xuan J
    BMC Bioinformatics; 2014; 15 Suppl 9(Suppl 9):S6. PubMed ID: 25252852
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.
    Dou Y; Guo X; Yuan L; Holding DR; Zhang C
    Biomed Res Int; 2015; 2015():789516. PubMed ID: 26339642
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Covariate-dependent negative binomial factor analysis of RNA sequencing data.
    Zamani Dadaneh S; Zhou M; Qian X
    Bioinformatics; 2018 Jul; 34(13):i61-i69. PubMed ID: 29949981
    [TBL] [Abstract][Full Text] [Related]  

  • 15. EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.
    Leng N; Li Y; McIntosh BE; Nguyen BK; Duffin B; Tian S; Thomson JA; Dewey CN; Stewart R; Kendziorski C
    Bioinformatics; 2015 Aug; 31(16):2614-22. PubMed ID: 25847007
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Accurate estimation of expression levels of homologous genes in RNA-seq experiments.
    Paşaniuc B; Zaitlen N; Halperin E
    J Comput Biol; 2011 Mar; 18(3):459-68. PubMed ID: 21385047
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates.
    Tuerk A; Wiktorin G; Güler S
    PLoS Comput Biol; 2017 May; 13(5):e1005515. PubMed ID: 28505151
    [TBL] [Abstract][Full Text] [Related]  

  • 18. What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment.
    Cui S; Ji T; Li J; Cheng J; Qiu J
    Stat Appl Genet Mol Biol; 2016 Apr; 15(2):87-105. PubMed ID: 26926865
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Library preparation methods for next-generation sequencing: tone down the bias.
    van Dijk EL; Jaszczyszyn Y; Thermes C
    Exp Cell Res; 2014 Mar; 322(1):12-20. PubMed ID: 24440557
    [TBL] [Abstract][Full Text] [Related]  

  • 20. In Silico HLA Typing Using Standard RNA-Seq Sequence Reads.
    Boegel S; Scholtalbers J; Löwer M; Sahin U; Castle JC
    Methods Mol Biol; 2015; 1310():247-58. PubMed ID: 26024640
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.