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 *

132 related articles for article (PubMed ID: 32938365)

  • 1. Model-based clustering for flow and mass cytometry data with clinical information.
    Abe K; Minoura K; Maeda Y; Nishikawa H; Shimamura T
    BMC Bioinformatics; 2020 Sep; 21(Suppl 13):393. PubMed ID: 32938365
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
    Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data.
    Minoura K; Abe K; Maeda Y; Nishikawa H; Shimamura T
    Bioinformatics; 2021 Jul; 37(11):1632-1634. PubMed ID: 33051653
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. GeoWaVe: geometric median clustering with weighted voting for ensemble clustering of cytometry data.
    Burton RJ; Cuff SM; Morgan MP; Artemiou A; Eberl M
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36413065
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A latent allocation model for the analysis of microbial composition and disease.
    Abe K; Hirayama M; Ohno K; Shimamura T
    BMC Bioinformatics; 2018 Dec; 19(Suppl 19):519. PubMed ID: 30598099
    [TBL] [Abstract][Full Text] [Related]  

  • 13. CytoBackBone: an algorithm for merging of phenotypic information from different cytometric profiles.
    Leite Pereira A; Lambotte O; Le Grand R; Cosma A; Tchitchek N
    Bioinformatics; 2019 Oct; 35(20):4187-4189. PubMed ID: 30903138
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Predicting Cell Populations in Single Cell Mass Cytometry Data.
    Abdelaal T; van Unen V; Höllt T; Koning F; Reinders MJT; Mahfouz A
    Cytometry A; 2019 Jul; 95(7):769-781. PubMed ID: 30861637
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.
    Van Gassen S; Callebaut B; Van Helden MJ; Lambrecht BN; Demeester P; Dhaene T; Saeys Y
    Cytometry A; 2015 Jul; 87(7):636-45. PubMed ID: 25573116
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.