BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

212 related articles for article (PubMed ID: 31484546)

  • 1. TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis.
    Li Z; Wu H
    Genome Biol; 2019 Sep; 20(1):190. PubMed ID: 31484546
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Complete deconvolution of DNA methylation signals from complex tissues: a geometric approach.
    Zhang W; Wu H; Li Z
    Bioinformatics; 2021 May; 37(8):1052-1059. PubMed ID: 33135072
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Robust partial reference-free cell composition estimation from tissue expression.
    Li Z; Guo Z; Cheng Y; Jin P; Wu H
    Bioinformatics; 2020 Jun; 36(11):3431-3438. PubMed ID: 32167531
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.
    Decamps C; Privé F; Bacher R; Jost D; Waguet A; ; Houseman EA; Lurie E; Lutsik P; Milosavljevic A; Scherer M; Blum MGB; Richard M
    BMC Bioinformatics; 2020 Jan; 21(1):16. PubMed ID: 31931698
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A systematic assessment of cell type deconvolution algorithms for DNA methylation data.
    Song J; Kuan PF
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36242584
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Cell-type deconvolution from DNA methylation: a review of recent applications.
    Titus AJ; Gallimore RM; Salas LA; Christensen BC
    Hum Mol Genet; 2017 Oct; 26(R2):R216-R224. PubMed ID: 28977446
    [TBL] [Abstract][Full Text] [Related]  

  • 7. imply: improving cell-type deconvolution accuracy using personalized reference profiles.
    Meng G; Pan Y; Tang W; Zhang L; Cui Y; Schumacher FR; Wang M; Wang R; He S; Krischer J; Li Q; Feng H
    Genome Med; 2024 Apr; 16(1):65. PubMed ID: 38685057
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells.
    Chiu YJ; Hsieh YH; Huang YH
    BMC Med Genomics; 2019 Dec; 12(Suppl 8):169. PubMed ID: 31856824
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz.
    Scherer M; Nazarov PV; Toth R; Sahay S; Kaoma T; Maurer V; Vedeneev N; Plass C; Lengauer T; Walter J; Lutsik P
    Nat Protoc; 2020 Oct; 15(10):3240-3263. PubMed ID: 32978601
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects.
    Houseman EA; Kile ML; Christiani DC; Ince TA; Kelsey KT; Marsit CJ
    BMC Bioinformatics; 2016 Jun; 17():259. PubMed ID: 27358049
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data.
    Gervin K; Salas LA; Bakulski KM; van Zelm MC; Koestler DC; Wiencke JK; Duijts L; Moll HA; Kelsey KT; Kobor MS; Lyle R; Christensen BC; Felix JF; Jones MJ
    Clin Epigenetics; 2019 Aug; 11(1):125. PubMed ID: 31455416
    [TBL] [Abstract][Full Text] [Related]  

  • 12.
    Meng G; Pan Y; Tang W; Zhang L; Cui Y; Schumacher FR; Wang M; Wang R; He S; Krischer J; Li Q; Feng H
    bioRxiv; 2023 Sep; ():. PubMed ID: 37808714
    [TBL] [Abstract][Full Text] [Related]  

  • 13.
    Maden SK; Huuki-Myers LA; Kwon SH; Collado-Torres L; Maynard KR; Hicks SC
    bioRxiv; 2024 Apr; ():. PubMed ID: 38617294
    [TBL] [Abstract][Full Text] [Related]  

  • 14. High-specificity bioinformatics framework for epigenomic profiling of discordant twins reveals specific and shared markers for ACPA and ACPA-positive rheumatoid arthritis.
    Gomez-Cabrero D; Almgren M; Sjöholm LK; Hensvold AH; Ringh MV; Tryggvadottir R; Kere J; Scheynius A; Acevedo N; Reinius L; Taub MA; Montano C; Aryee MJ; Feinberg JI; Feinberg AP; Tegnér J; Klareskog L; Catrina AI; Ekström TJ
    Genome Med; 2016 Nov; 8(1):124. PubMed ID: 27876072
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.
    McGregor K; Bernatsky S; Colmegna I; Hudson M; Pastinen T; Labbe A; Greenwood CM
    Genome Biol; 2016 May; 17():84. PubMed ID: 27142380
    [TBL] [Abstract][Full Text] [Related]  

  • 16. EMeth: An EM algorithm for cell type decomposition based on DNA methylation data.
    Zhang H; Cai R; Dai J; Sun W
    Sci Rep; 2021 Mar; 11(1):5717. PubMed ID: 33707472
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE.
    Zhu T; Teschendorff AE
    Methods Mol Biol; 2023; 2629():23-42. PubMed ID: 36929072
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computational deconvolution of DNA methylation data from mixed DNA samples.
    Ferro Dos Santos MR; Giuili E; De Koker A; Everaert C; De Preter K
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38762790
    [TBL] [Abstract][Full Text] [Related]  

  • 19. debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues.
    Chen L; Wu CT; Wang N; Herrington DM; Clarke R; Wang Y
    Bioinformatics; 2020 Jun; 36(12):3927-3929. PubMed ID: 32219387
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deconvolution of tumor composition using partially available DNA methylation data.
    He D; Chen M; Wang W; Song C; Qin Y
    BMC Bioinformatics; 2022 Aug; 23(1):355. PubMed ID: 36002797
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.