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 *

122 related articles for article (PubMed ID: 38390437)

  • 1. NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.
    Liefeld T; Huang E; Wenzel AT; Yoshimoto K; Sharma AK; Sicklick JK; Mesirov JP; Reich M
    J Bioinform Syst Biol; 2023; 6(4):379-383. PubMed ID: 38390437
    [TBL] [Abstract][Full Text] [Related]  

  • 2. NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration.
    Liefeld T; Huang E; Wenzel AT; Yoshimoto K; Sharma AK; Sicklick JK; Mesirov JP; Reich M
    bioRxiv; 2023 Jun; ():. PubMed ID: 37398372
    [TBL] [Abstract][Full Text] [Related]  

  • 3. NMF-mGPU: non-negative matrix factorization on multi-GPU systems.
    Mejía-Roa E; Tabas-Madrid D; Setoain J; García C; Tirado F; Pascual-Montano A
    BMC Bioinformatics; 2015 Feb; 16():43. PubMed ID: 25887585
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A robust semi-supervised NMF model for single cell RNA-seq data.
    Wu P; An M; Zou HR; Zhong CY; Wang W; Wu CP
    PeerJ; 2020; 8():e10091. PubMed ID: 33088619
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization.
    Zhu X; Ching T; Pan X; Weissman SM; Garmire L
    PeerJ; 2017; 5():e2888. PubMed ID: 28133571
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.
    Johnson JAI; Tsang AP; Mitchell JT; Zhou DL; Bowden J; Davis-Marcisak E; Sherman T; Liefeld T; Loth M; Goff LA; Zimmerman JW; Kinny-Köster B; Jaffee EM; Tamayo P; Mesirov JP; Reich M; Fertig EJ; Stein-O'Brien GL
    Nat Protoc; 2023 Dec; 18(12):3690-3731. PubMed ID: 37989764
    [TBL] [Abstract][Full Text] [Related]  

  • 8. scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data.
    Wang Z; Wang H; Zhao J; Zheng C
    BMC Bioinformatics; 2023 May; 24(1):217. PubMed ID: 37237310
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF.
    Venkatasubramanian M; Chetal K; Schnell DJ; Atluri G; Salomonis N
    Bioinformatics; 2020 Jun; 36(12):3773-3780. PubMed ID: 32207533
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data.
    Qiu Y; Yan C; Zhao P; Zou Q
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37122068
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization.
    Hozumi Y; Wei GW
    J Comput Appl Math; 2024 Aug; 445():. PubMed ID: 38464901
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Robust Graph Regularized NMF with Dissimilarity and Similarity Constraints for ScRNA-seq Data Clustering.
    Shu Z; Long Q; Zhang L; Yu Z; Wu XJ
    J Chem Inf Model; 2022 Dec; 62(23):6271-6286. PubMed ID: 36459053
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SC-JNMF: single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization.
    Shiga M; Seno S; Onizuka M; Matsuda H
    PeerJ; 2021; 9():e12087. PubMed ID: 34532161
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization.
    Wang CY; Gao YL; Kong XZ; Liu JX; Zheng CH
    IEEE J Biomed Health Inform; 2022 Jan; 26(1):458-467. PubMed ID: 34156956
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.
    Gan Y; Chen Y; Xu G; Guo W; Zou G
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37313714
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis.
    Liu W; Yuan K; Ye D
    J Biomed Inform; 2008 Aug; 41(4):602-6. PubMed ID: 18234564
    [TBL] [Abstract][Full Text] [Related]  

  • 17. JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering.
    Lan W; Liu M; Chen J; Ye J; Zheng R; Zhu X; Peng W
    Methods; 2024 Feb; 222():1-9. PubMed ID: 38128706
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Robust classification of single-cell transcriptome data by nonnegative matrix factorization.
    Shao C; Höfer T
    Bioinformatics; 2017 Jan; 33(2):235-242. PubMed ID: 27663498
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Hessian regularization based non-negative matrix factorization for gene expression data clustering.
    Liu X; Shi J; Wang C
    Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():4130-3. PubMed ID: 26737203
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.