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 *

115 related articles for article (PubMed ID: 33476090)

  • 1. Tailor: Targeting heavy tails in flow cytometry data with fast, interpretable mixture modeling.
    Ionita M; Schretzenmair R; Jones D; Moore J; Wang LS; Rogers W
    Cytometry A; 2021 Feb; 99(2):133-144. PubMed ID: 33476090
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Scalable clustering algorithms for continuous environmental flow cytometry.
    Hyrkas J; Clayton S; Ribalet F; Halperin D; Armbrust EV; Howe B
    Bioinformatics; 2016 Feb; 32(3):417-23. PubMed ID: 26476780
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automated gating of flow cytometry data via robust model-based clustering.
    Lo K; Brinkman RR; Gottardo R
    Cytometry A; 2008 Apr; 73(4):321-32. PubMed ID: 18307272
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Determination of essential phenotypic elements of clusters in high-dimensional entities-DEPECHE.
    Theorell A; Bryceson YT; Theorell J
    PLoS One; 2019; 14(3):e0203247. PubMed ID: 30845234
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data.
    Platon L; Pejoski D; Gautreau G; Targat B; Le Grand R; Beignon AS; Tchitchek N
    Methods; 2018 Jan; 132():66-75. PubMed ID: 28917725
    [TBL] [Abstract][Full Text] [Related]  

  • 6. flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.
    Ge Y; Sealfon SC
    Bioinformatics; 2012 Aug; 28(15):2052-8. PubMed ID: 22595209
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Misty Mountain clustering: application to fast unsupervised flow cytometry gating.
    Sugár IP; Sealfon SC
    BMC Bioinformatics; 2010 Oct; 11():502. PubMed ID: 20932336
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Ultrafast clustering of single-cell flow cytometry data using FlowGrid.
    Ye X; Ho JWK
    BMC Syst Biol; 2019 Apr; 13(Suppl 2):35. PubMed ID: 30953498
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
    Weber LM; Robinson MD
    Cytometry A; 2016 Dec; 89(12):1084-1096. PubMed ID: 27992111
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure.
    Lee SX; McLachlan GJ; Pyne S
    Cytometry A; 2016 Jan; 89(1):30-43. PubMed ID: 26492316
    [TBL] [Abstract][Full Text] [Related]  

  • 11. GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry.
    Suwalska A; Polanska J
    Int J Mol Sci; 2023 Sep; 24(18):. PubMed ID: 37762336
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Model-based cell clustering and population tracking for time-series flow cytometry data.
    Minoura K; Abe K; Maeda Y; Nishikawa H; Shimamura T
    BMC Bioinformatics; 2019 Dec; 20(Suppl 23):633. PubMed ID: 31881827
    [TBL] [Abstract][Full Text] [Related]  

  • 13. immunoClust--An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets.
    Sörensen T; Baumgart S; Durek P; Grützkau A; Häupl T
    Cytometry A; 2015 Jul; 87(7):603-15. PubMed ID: 25850678
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Competitive SWIFT cluster templates enhance detection of aging changes.
    Rebhahn JA; Roumanes DR; Qi Y; Khan A; Thakar J; Rosenberg A; Lee FE; Quataert SA; Sharma G; Mosmann TR
    Cytometry A; 2016 Jan; 89(1):59-70. PubMed ID: 26441030
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.
    Ashhurst TM; Marsh-Wakefield F; Putri GH; Spiteri AG; Shinko D; Read MN; Smith AL; King NJC
    Cytometry A; 2022 Mar; 101(3):237-253. PubMed ID: 33840138
    [TBL] [Abstract][Full Text] [Related]  

  • 16. FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data.
    Rogers WT; Holyst HA
    Adv Bioinformatics; 2009; 2009():193947. PubMed ID: 19956416
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.
    Rubbens P; Props R; Kerckhof FM; Boon N; Waegeman W
    mSphere; 2021 Feb; 6(1):. PubMed ID: 33536320
    [TBL] [Abstract][Full Text] [Related]  

  • 18. optimalFlow: optimal transport approach to flow cytometry gating and population matching.
    Del Barrio E; Inouzhe H; Loubes JM; Matrán C; Mayo-Íscar A
    BMC Bioinformatics; 2020 Oct; 21(1):479. PubMed ID: 33109072
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SPADEVizR: an R package for visualization, analysis and integration of SPADE results.
    Gautreau G; Pejoski D; Le Grand R; Cosma A; Beignon AS; Tchitchek N
    Bioinformatics; 2017 Mar; 33(5):779-781. PubMed ID: 27993789
    [TBL] [Abstract][Full Text] [Related]  

  • 20. CytoNorm: A Normalization Algorithm for Cytometry Data.
    Van Gassen S; Gaudilliere B; Angst MS; Saeys Y; Aghaeepour N
    Cytometry A; 2020 Mar; 97(3):268-278. PubMed ID: 31633883
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.