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 *

118 related articles for article (PubMed ID: 28759711)

  • 1. What is a "unimodal" cell population? Using statistical tests as criteria for unimodality in automated gating and quality control.
    Johnsson K; Linderoth M; Fontes M
    Cytometry A; 2017 Sep; 91(9):908-916. PubMed ID: 28759711
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Critical assessment of automated flow cytometry data analysis techniques.
    Aghaeepour N; Finak G; ; ; Hoos H; Mosmann TR; Brinkman R; Gottardo R; Scheuermann RH
    Nat Methods; 2013 Mar; 10(3):228-38. PubMed ID: 23396282
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure.
    Hsiao C; Liu M; Stanton R; McGee M; Qian Y; Scheuermann RH
    Cytometry A; 2016 Jan; 89(1):71-88. PubMed ID: 26274018
    [TBL] [Abstract][Full Text] [Related]  

  • 4. cytometree: A binary tree algorithm for automatic gating in cytometry analysis.
    Commenges D; Alkhassim C; Gottardo R; Hejblum B; Thiébaut R
    Cytometry A; 2018 Nov; 93(11):1132-1140. PubMed ID: 30277649
    [TBL] [Abstract][Full Text] [Related]  

  • 5. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.
    Lee AJ; Chang I; Burel JG; Lindestam Arlehamn CS; Mandava A; Weiskopf D; Peters B; Sette A; Scheuermann RH; Qian Y
    Cytometry A; 2018 Jun; 93(6):597-610. PubMed ID: 29665244
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automatically generate two-dimensional gating hierarchy from clustered cytometry data.
    Yang X; Qiu P
    Cytometry A; 2018 Oct; 93(10):1039-1050. PubMed ID: 30176185
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Data-Driven Flow Cytometry Analysis.
    Wang S; Brinkman RR
    Methods Mol Biol; 2019; 1989():245-265. PubMed ID: 31077110
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Computational prediction of manually gated rare cells in flow cytometry data.
    Qiu P
    Cytometry A; 2015 Jul; 87(7):594-602. PubMed ID: 25755118
    [TBL] [Abstract][Full Text] [Related]  

  • 9. High throughput automated analysis of big flow cytometry data.
    Rahim A; Meskas J; Drissler S; Yue A; Lorenc A; Laing A; Saran N; White J; Abeler-Dörner L; Hayday A; Brinkman RR
    Methods; 2018 Feb; 134-135():164-176. PubMed ID: 29287915
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automated analysis of flow cytometry data comes of age.
    Brinkman RR; Aghaeepour N; Finak G; Gottardo R; Mosmann T; Scheuermann RH
    Cytometry A; 2016 Jan; 89(1):13-5. PubMed ID: 26812230
    [No Abstract]   [Full Text] [Related]  

  • 11. Optimizing transformations for automated, high throughput analysis of flow cytometry data.
    Finak G; Perez JM; Weng A; Gottardo R
    BMC Bioinformatics; 2010 Nov; 11():546. PubMed ID: 21050468
    [TBL] [Abstract][Full Text] [Related]  

  • 12. FloReMi: Flow density survival regression using minimal feature redundancy.
    Van Gassen S; Vens C; Dhaene T; Lambrecht BN; Saeys Y
    Cytometry A; 2016 Jan; 89(1):22-9. PubMed ID: 26243673
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Mixture modeling approach to flow cytometry data.
    Boedigheimer MJ; Ferbas J
    Cytometry A; 2008 May; 73(5):421-9. PubMed ID: 18383311
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument compatible probability state model.
    Wong L; Hill BL; Hunsberger BC; Bagwell CB; Curtis AD; Davis BH
    Cytometry B Clin Cytom; 2015; 88(4):227-35. PubMed ID: 25529112
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.
    Weber LM; Nowicka M; Soneson C; Robinson MD
    Commun Biol; 2019; 2():183. PubMed ID: 31098416
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.
    Aghaeepour N; Chattopadhyay P; Chikina M; Dhaene T; Van Gassen S; Kursa M; Lambrecht BN; Malek M; McLachlan GJ; Qian Y; Qiu P; Saeys Y; Stanton R; Tong D; Vens C; Walkowiak S; Wang K; Finak G; Gottardo R; Mosmann T; Nolan GP; Scheuermann RH; Brinkman RR
    Cytometry A; 2016 Jan; 89(1):16-21. PubMed ID: 26447924
    [TBL] [Abstract][Full Text] [Related]  

  • 19. BayesFlow: latent modeling of flow cytometry cell populations.
    Johnsson K; Wallin J; Fontes M
    BMC Bioinformatics; 2016 Jan; 17():25. PubMed ID: 26755197
    [TBL] [Abstract][Full Text] [Related]  

  • 20. QFMatch: multidimensional flow and mass cytometry samples alignment.
    Orlova DY; Meehan S; Parks D; Moore WA; Meehan C; Zhao Q; Ghosn EEB; Herzenberg LA; Walther G
    Sci Rep; 2018 Feb; 8(1):3291. PubMed ID: 29459702
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.