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 *

181 related articles for article (PubMed ID: 39207729)

  • 1. CHAI: consensus clustering through similarity matrix integration for cell-type identification.
    Lodi MK; Lodi M; Osei K; Ranganathan V; Hwang P; Ghosh P
    Brief Bioinform; 2024 Jul; 25(5):. PubMed ID: 39207729
    [TBL] [Abstract][Full Text] [Related]  

  • 2. CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.
    Lodi MK; Lodi M; Osei K; Ranganathan V; Hwang P; Ghosh P
    bioRxiv; 2024 Mar; ():. PubMed ID: 38562750
    [TBL] [Abstract][Full Text] [Related]  

  • 3. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.
    Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X
    Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq.
    Zhang JM; Kamath GM; Tse DN
    Cell Syst; 2019 Oct; 9(4):383-392.e6. PubMed ID: 31521605
    [TBL] [Abstract][Full Text] [Related]  

  • 6. scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets.
    Song Q; Su J; Miller LD; Zhang W
    Genomics Proteomics Bioinformatics; 2021 Apr; 19(2):330-341. PubMed ID: 33359676
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Spectral clustering based on learning similarity matrix.
    Park S; Zhao H
    Bioinformatics; 2018 Jun; 34(12):2069-2076. PubMed ID: 29432517
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.
    Liang X; Cao L; Chen H; Wang L; Wang Y; Fu L; Tan X; Chen E; Ding Y; Tang J
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38168839
    [TBL] [Abstract][Full Text] [Related]  

  • 9. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.
    Chen S; Hua K; Cui H; Jiang R
    BMC Bioinformatics; 2019 May; 20(Suppl 7):0. PubMed ID: 31074382
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.
    Gao Q; Ji Z; Wang L; Owzar K; Li QJ; Chan C; Xie J
    Nucleic Acids Res; 2024 May; 52(9):e46. PubMed ID: 38647069
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.
    Mallick K; Chakraborty S; Mallik S; Bandyopadhyay S
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37185897
    [TBL] [Abstract][Full Text] [Related]  

  • 12. scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.
    Zhai Y; Chen L; Deng M
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38678389
    [TBL] [Abstract][Full Text] [Related]  

  • 13. CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.
    Wang L; Hong C; Song J; Yao J
    Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38552307
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments.
    Ekiz HA; Conley CJ; Stephens WZ; O'Connell RM
    BMC Bioinformatics; 2020 May; 21(1):191. PubMed ID: 32414321
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data.
    Yu L; Cao Y; Yang JYH; Yang P
    Genome Biol; 2022 Feb; 23(1):49. PubMed ID: 35135612
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An interpretable framework for clustering single-cell RNA-Seq datasets.
    Zhang JM; Fan J; Fan HC; Rosenfeld D; Tse DN
    BMC Bioinformatics; 2018 Mar; 19(1):93. PubMed ID: 29523077
    [TBL] [Abstract][Full Text] [Related]  

  • 17. FlowGrid enables fast clustering of very large single-cell RNA-seq data.
    Fang X; Ho JWK
    Bioinformatics; 2021 Dec; 38(1):282-283. PubMed ID: 34289014
    [TBL] [Abstract][Full Text] [Related]  

  • 18. SCMarker: Ab initio marker selection for single cell transcriptome profiling.
    Wang F; Liang S; Kumar T; Navin N; Chen K
    PLoS Comput Biol; 2019 Oct; 15(10):e1007445. PubMed ID: 31658262
    [TBL] [Abstract][Full Text] [Related]  

  • 19. clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.
    Risso D; Purvis L; Fletcher RB; Das D; Ngai J; Dudoit S; Purdom E
    PLoS Comput Biol; 2018 Sep; 14(9):e1006378. PubMed ID: 30180157
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
    Mieth B; Hockley JRF; Görnitz N; Vidovic MM; Müller KR; Gutteridge A; Ziemek D
    Sci Rep; 2019 Dec; 9(1):20353. PubMed ID: 31889137
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.