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 *

154 related articles for article (PubMed ID: 39018275)

  • 1. Differentially expressed heterogeneous overdispersion genes testing for count data.
    Yuan Y; Xu Q; Wani A; Dahrendorff J; Wang C; Shen A; Donglasan J; Burgan S; Graham Z; Uddin M; Wildman D; Qu A
    PLoS One; 2024; 19(7):e0300565. PubMed ID: 39018275
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data.
    Yuan Y; Xu Q; Wani A; Dahrendor J; Wang C; Donglasan J; Burgan S; Graham Z; Uddin M; Wildman D; Qu A
    bioRxiv; 2023 Feb; ():. PubMed ID: 36865247
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A two-step integrated approach to detect differentially expressed genes in RNA-Seq data.
    Al Mahi N; Begum M
    J Bioinform Comput Biol; 2016 Dec; 14(6):1650034. PubMed ID: 27774870
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Robust identification of differentially expressed genes from RNA-seq data.
    Shahjaman M; Manir Hossain Mollah M; Rezanur Rahman M; Islam SMS; Nurul Haque Mollah M
    Genomics; 2020 Mar; 112(2):2000-2010. PubMed ID: 31756426
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Detecting differentially expressed genes by smoothing effect of gene length on variance estimation.
    Tang J; Wang F
    J Bioinform Comput Biol; 2015 Dec; 13(6):1542004. PubMed ID: 26608751
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data.
    Baik B; Yoon S; Nam D
    PLoS One; 2020; 15(4):e0232271. PubMed ID: 32353015
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii.
    Raithel S; Johnson L; Galliart M; Brown S; Shelton J; Herndon N; Bello NM
    BMC Genomics; 2016 Feb; 17():140. PubMed ID: 26919855
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses.
    Han G; Yan D; Sun Z; Fang J; Chang X; Wilson L; Liu Y
    Hum Genomics; 2024 Jun; 18(1):69. PubMed ID: 38902839
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.
    Lund SP; Nettleton D; McCarthy DJ; Smyth GK
    Stat Appl Genet Mol Biol; 2012 Oct; 11(5):. PubMed ID: 23104842
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Evaluation of methods for differential expression analysis on multi-group RNA-seq count data.
    Tang M; Sun J; Shimizu K; Kadota K
    BMC Bioinformatics; 2015 Nov; 16():361. PubMed ID: 26538400
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Modeling overdispersion heterogeneity in differential expression analysis using mixtures.
    Bonafede E; Picard F; Robin S; Viroli C
    Biometrics; 2016 Sep; 72(3):804-14. PubMed ID: 26683201
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes.
    Deyneko IV; Mustafaev ON; Tyurin AА; Zhukova KV; Varzari A; Goldenkova-Pavlova IV
    BMC Bioinformatics; 2022 Nov; 23(1):488. PubMed ID: 36384457
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data.
    Zhu B; Li H; Zhang L; Chandra SS; Zhao H
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35514182
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.
    Liu K; Ye J; Yang Y; Shen L; Jiang H
    IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(2):442-454. PubMed ID: 29993952
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.