BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

168 related articles for article (PubMed ID: 38589921)

  • 21. scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means.
    Wang X; Gao H; Qi R; Zheng R; Gao X; Yu B
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):2007-2015. PubMed ID: 37015596
    [TBL] [Abstract][Full Text] [Related]  

  • 22. BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.
    Zhan X; Yin Y; Zhang H
    Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38439545
    [TBL] [Abstract][Full Text] [Related]  

  • 23. 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]  

  • 24. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
    Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
    Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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]  

  • 26. Scbean: a python library for single-cell multi-omics data analysis.
    Zhang H; Wang Y; Lian B; Wang Y; Li X; Wang T; Shang X; Yang H; Aziz A; Hu J
    Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38290765
    [TBL] [Abstract][Full Text] [Related]  

  • 27. IsoCell: An Approach to Enhance Single Cell Clustering by Integrating Isoform-Level Expression Through Orthogonal Projection.
    Liu Y; Li HD; Xu Y; Liu YW; Peng X; Wang J
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):465-475. PubMed ID: 35100120
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization.
    Jin K; Li B; Yan H; Zhang XF
    Bioinformatics; 2022 Jun; 38(12):3222-3230. PubMed ID: 35485740
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. Scaling up single-cell RNA-seq data analysis with CellBridge workflow.
    Nouri N; Kurlovs AH; Gaglia G; de Rinaldis E; Savova V
    Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38113416
    [TBL] [Abstract][Full Text] [Related]  

  • 31. BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data.
    Boggy GJ; McElfresh GW; Mahyari E; Ventura AB; Hansen SG; Picker LJ; Bimber BN
    Bioinformatics; 2022 May; 38(10):2791-2801. PubMed ID: 35561167
    [TBL] [Abstract][Full Text] [Related]  

  • 32. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.
    Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA
    Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008
    [TBL] [Abstract][Full Text] [Related]  

  • 33. ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes.
    Iida K; Kondo J; Wibisana JN; Inoue M; Okada M
    Bioinformatics; 2022 Sep; 38(18):4330-4336. PubMed ID: 35924984
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Scellpam: an R package/C++ library to perform parallel partitioning around medoids on scRNAseq data sets.
    Domingo J; Leon T; Dura E
    BMC Bioinformatics; 2023 Sep; 24(1):342. PubMed ID: 37710192
    [TBL] [Abstract][Full Text] [Related]  

  • 35. NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data.
    Agostinis F; Romualdi C; Sales G; Risso D
    Bioinformatics; 2022 Apr; 38(9):2648-2650. PubMed ID: 35266509
    [TBL] [Abstract][Full Text] [Related]  

  • 36. scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data.
    Song D; Xi NM; Li JJ; Wang L
    Bioinformatics; 2022 May; 38(11):3126-3127. PubMed ID: 35426898
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Hopper: a mathematically optimal algorithm for sketching biological data.
    DeMeo B; Berger B
    Bioinformatics; 2020 Jul; 36(Suppl_1):i236-i241. PubMed ID: 32657375
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Phitest for analyzing the homogeneity of single-cell populations.
    Li WV
    Bioinformatics; 2022 Apr; 38(9):2639-2641. PubMed ID: 35238346
    [TBL] [Abstract][Full Text] [Related]  

  • 39. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.
    Lin P; Troup M; Ho JW
    Genome Biol; 2017 Mar; 18(1):59. PubMed ID: 28351406
    [TBL] [Abstract][Full Text] [Related]  

  • 40. MultiBaC: an R package to remove batch effects in multi-omic experiments.
    Ugidos M; Nueda MJ; Prats-Montalbán JM; Ferrer A; Conesa A; Tarazona S
    Bioinformatics; 2022 Apr; 38(9):2657-2658. PubMed ID: 35238331
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.