BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

137 related articles for article (PubMed ID: 35855951)

  • 1. ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data.
    Senra D; Guisoni N; Diambra L
    MethodsX; 2022; 9():101778. PubMed ID: 35855951
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Cell annotation using scRNA-seq data: A protein-protein interaction network approach.
    Senra D; Guisoni N; Diambra L
    MethodsX; 2023; 10():102179. PubMed ID: 37128282
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.
    Lasri A; Shahrezaei V; Sturrock M
    BMC Bioinformatics; 2022 Jun; 23(1):236. PubMed ID: 35715748
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Quantifying pluripotency landscape of cell differentiation from scRNA-seq data by continuous birth-death process.
    Shi J; Li T; Chen L; Aihara K
    PLoS Comput Biol; 2019 Nov; 15(11):e1007488. PubMed ID: 31721764
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Determining cellular lineage directed networks in hematopoiesis using single-cell transcriptomic data and volatility-constrained correlation.
    Ochiai T; Nacher JC
    Biosystems; 2024 Jun; 242():105248. PubMed ID: 38871242
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.
    Dong X; Leary JR; Yang C; Brusko MA; Brusko TM; Bacher R
    bioRxiv; 2023 Dec; ():. PubMed ID: 38187768
    [TBL] [Abstract][Full Text] [Related]  

  • 8. RNA-Seq analysis reveals pluripotency-associated genes and their interaction networks in human embryonic stem cells.
    Ghosh A; Som A
    Comput Biol Chem; 2020 Apr; 85():107239. PubMed ID: 32109853
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Uncovering cellular networks in branching morphogenesis using single-cell transcriptomics.
    Goodwin K; Nelson CM
    Curr Top Dev Biol; 2021; 143():239-280. PubMed ID: 33820623
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Inference of differentiation time for single cell transcriptomes using cell population reference data.
    Sun N; Yu X; Li F; Liu D; Suo S; Chen W; Chen S; Song L; Green CD; McDermott J; Shen Q; Jing N; Han JJ
    Nat Commun; 2017 Nov; 8(1):1856. PubMed ID: 29187729
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.
    Ghannoum S; Leoncio Netto W; Fantini D; Ragan-Kelley B; Parizadeh A; Jonasson E; Ståhlberg A; Farhan H; Köhn-Luque A
    Int J Mol Sci; 2021 Jan; 22(3):. PubMed ID: 33573289
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data.
    Qi Y; Guo Y; Jiao H; Shang X
    BMC Bioinformatics; 2020 Jun; 21(1):240. PubMed ID: 32527285
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
    Wang W; Tan H; Sun M; Han Y; Chen W; Qiu S; Zheng K; Wei G; Ni T
    Nucleic Acids Res; 2021 May; 49(9):e54. PubMed ID: 33619563
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.
    Slovin S; Carissimo A; Panariello F; Grimaldi A; Bouché V; Gambardella G; Cacchiarelli D
    Methods Mol Biol; 2021; 2284():343-365. PubMed ID: 33835452
    [TBL] [Abstract][Full Text] [Related]  

  • 16. scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data.
    Zheng X; Huang Y; Zou X
    PLoS Comput Biol; 2020 Jul; 16(7):e1007471. PubMed ID: 32716923
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.
    Aubin-Frankowski PC; Vert JP
    Bioinformatics; 2020 Sep; 36(18):4774-4780. PubMed ID: 33026066
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN.
    Gan Y; Hu X; Zou G; Yan C; Xu G
    Front Oncol; 2022; 12():899825. PubMed ID: 35692809
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.
    Tran TN; Bader GD
    PLoS Comput Biol; 2020 Sep; 16(9):e1008205. PubMed ID: 32903255
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data.
    Torroja C; Sanchez-Cabo F
    Front Genet; 2019; 10():978. PubMed ID: 31708961
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.