BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

252 related articles for article (PubMed ID: 37295843)

  • 1. Debiased personalized gene coexpression networks for population-scale scRNA-seq data.
    Lu S; Keleş S
    Genome Res; 2023 Jun; 33(6):932-947. PubMed ID: 37295843
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data.
    Lu S; Keleş S
    bioRxiv; 2023 Apr; ():. PubMed ID: 37163070
    [TBL] [Abstract][Full Text] [Related]  

  • 3. MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing.
    Şapcı AOB; Lu S; Yan S; Ay F; Tastan O; Keleş S
    Bioinformatics; 2023 Oct; 39(10):. PubMed ID: 37740957
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications.
    Peng H; Zeng X; Zhou Y; Zhang D; Nussinov R; Cheng F
    PLoS Comput Biol; 2019 Feb; 15(2):e1006772. PubMed ID: 30779739
    [TBL] [Abstract][Full Text] [Related]  

  • 5. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network.
    Huang Z; Wang J; Lu X; Mohd Zain A; Yu G
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36733262
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Detection of high variability in gene expression from single-cell RNA-seq profiling.
    Chen HI; Jin Y; Huang Y; Chen Y
    BMC Genomics; 2016 Aug; 17 Suppl 7(Suppl 7):508. PubMed ID: 27556924
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.
    Wang Y; Tian X; Ai D
    Biomed Res Int; 2021; 2021():9919080. PubMed ID: 34095314
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing.
    Xu H; Hu Y; Zhang X; Aouizerat BE; Yan C; Xu K
    BMC Genomics; 2022 Jan; 23(1):35. PubMed ID: 34996359
    [TBL] [Abstract][Full Text] [Related]  

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

  • 12. scDMAE: A Generative Denoising Model Adopted Mask Strategy for scRNA-Seq Data Recovery.
    Liu W; Pan Y; Teng Z; Xu J
    IEEE J Biomed Health Inform; 2024 Jun; 28(6):3772-3780. PubMed ID: 38568766
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Robustness of single-cell RNA-seq for identifying differentially expressed genes.
    Liu Y; Huang J; Pandey R; Liu P; Therani B; Qiu Q; Rao S; Geurts AM; Cowley AW; Greene AS; Liang M
    BMC Genomics; 2023 Jul; 24(1):371. PubMed ID: 37394518
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.
    Wang YM; Sun Y; Wang B; Wu Z; He XY; Zhao Y
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 37991248
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.
    Chen S; Yan X; Zheng R; Li M
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36567258
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data.
    Mao G; Pang Z; Zuo K; Liu J
    J Comput Biol; 2023 May; 30(5):619-631. PubMed ID: 36877552
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mostly natural sequencing-by-synthesis for scRNA-seq using Ultima sequencing.
    Simmons SK; Lithwick-Yanai G; Adiconis X; Oberstrass F; Iremadze N; Geiger-Schuller K; Thakore PI; Frangieh CJ; Barad O; Almogy G; Rozenblatt-Rosen O; Regev A; Lipson D; Levin JZ
    Nat Biotechnol; 2023 Feb; 41(2):204-211. PubMed ID: 36109685
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine learning and statistical methods for clustering single-cell RNA-sequencing data.
    Petegrosso R; Li Z; Kuang R
    Brief Bioinform; 2020 Jul; 21(4):1209-1223. PubMed ID: 31243426
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories.
    Pokhilko A; Handel AE; Curion F; Volpato V; Whiteley ES; Bøstrand S; Newey SE; Akerman CJ; Webber C; Clark MB; Bowden R; Cader MZ
    Genome Res; 2021 Jun; 31(6):1069-1081. PubMed ID: 34011578
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.