BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

300 related articles for article (PubMed ID: 31992615)

  • 1. SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection.
    Wan S; Kim J; Won KJ
    Genome Res; 2020 Feb; 30(2):205-213. PubMed ID: 31992615
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
    Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
    BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation.
    Chen L; Zhai Y; He Q; Wang W; Deng M
    Genes (Basel); 2020 Jul; 11(7):. PubMed ID: 32674393
    [TBL] [Abstract][Full Text] [Related]  

  • 4. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
    Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data.
    Cheng C; Easton J; Rosencrance C; Li Y; Ju B; Williams J; Mulder HL; Pang Y; Chen W; Chen X
    Nucleic Acids Res; 2019 Dec; 47(22):e143. PubMed ID: 31566233
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data.
    Wu W; Ma X
    Bioinformatics; 2020 Jun; 36(12):3825-3832. PubMed ID: 32246821
    [TBL] [Abstract][Full Text] [Related]  

  • 7. ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.
    Malec M; Kurban H; Dalkilic M
    BMC Bioinformatics; 2022 Jul; 23(1):291. PubMed ID: 35869420
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Data Analysis in Single-Cell Transcriptome Sequencing.
    Gao S
    Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
    [TBL] [Abstract][Full Text] [Related]  

  • 9. JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.
    Cui T; Wang T
    BMC Genomics; 2021 Jan; 22(1):47. PubMed ID: 33430769
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges.
    Nie X; Qin D; Zhou X; Duo H; Hao Y; Li B; Liang G
    Comput Biol Med; 2023 Jun; 159():106939. PubMed ID: 37075602
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.
    Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S
    Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improving Single-Cell RNA-seq Clustering by Integrating Pathways.
    Zhang C; Gao L; Wang B; Gao Y
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 33940590
    [TBL] [Abstract][Full Text] [Related]  

  • 13. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
    Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
    BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis.
    Zhang Y; Kim MS; Reichenberger ER; Stear B; Taylor DM
    PLoS Comput Biol; 2020 Apr; 16(4):e1007794. PubMed ID: 32339163
    [TBL] [Abstract][Full Text] [Related]  

  • 15. SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.
    Ren X; Zheng L; Zhang Z
    Genomics Proteomics Bioinformatics; 2019 Apr; 17(2):201-210. PubMed ID: 31202000
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improving replicability in single-cell RNA-Seq cell type discovery with Dune.
    Roux de BĂ©zieux H; Street K; Fischer S; Van den Berge K; Chance R; Risso D; Gillis J; Ngai J; Purdom E; Dudoit S
    BMC Bioinformatics; 2024 May; 25(1):198. PubMed ID: 38789920
    [TBL] [Abstract][Full Text] [Related]  

  • 17. geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.
    Missarova A; Jain J; Butler A; Ghazanfar S; Stuart T; Brusko M; Wasserfall C; Nick H; Brusko T; Atkinson M; Satija R; Marioni JC
    Genome Biol; 2021 Dec; 22(1):333. PubMed ID: 34872616
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
    Feng C; Liu S; Zhang H; Guan R; Li D; Zhou F; Liang Y; Feng X
    Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32235704
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Evaluation of single-cell classifiers for single-cell RNA sequencing data sets.
    Zhao X; Wu S; Fang N; Sun X; Fan J
    Brief Bioinform; 2020 Sep; 21(5):1581-1595. PubMed ID: 31675098
    [TBL] [Abstract][Full Text] [Related]  

  • 20. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.
    Stassen SV; Siu DMD; Lee KCM; Ho JWK; So HKH; Tsia KK
    Bioinformatics; 2020 May; 36(9):2778-2786. PubMed ID: 31971583
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.