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 *

202 related articles for article (PubMed ID: 39406498)

  • 1. Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.
    Petrany A; Chen R; Zhang S; Chen Y
    Genome Res; 2024 Oct; 34(10):1636-1650. PubMed ID: 39406498
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance.
    Zhang S; Xie L; Cui Y; Carone BR; Chen Y
    Biomolecules; 2022 Aug; 12(8):. PubMed ID: 36009024
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Bias, robustness and scalability in single-cell differential expression analysis.
    Soneson C; Robinson MD
    Nat Methods; 2018 Apr; 15(4):255-261. PubMed ID: 29481549
    [TBL] [Abstract][Full Text] [Related]  

  • 5. An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.
    Mah CK; Wenzel AT; Juarez EF; Tabor T; Reich MM; Mesirov JP
    F1000Res; 2018; 7():1306. PubMed ID: 31316748
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.
    Esnaola M; Puig P; Gonzalez D; Castelo R; Gonzalez JR
    BMC Bioinformatics; 2013 Aug; 14():254. PubMed ID: 23965047
    [TBL] [Abstract][Full Text] [Related]  

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

  • 8. DEsingle for detecting three types of differential expression in single-cell RNA-seq data.
    Miao Z; Deng K; Wang X; Zhang X
    Bioinformatics; 2018 Sep; 34(18):3223-3224. PubMed ID: 29688277
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Visualization of Single Cell RNA-Seq Data Using t-SNE in R.
    Zhou B; Jin W
    Methods Mol Biol; 2020; 2117():159-167. PubMed ID: 31960377
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Single-cell RNA-seq denoising using a deep count autoencoder.
    Eraslan G; Simon LM; Mircea M; Mueller NS; Theis FJ
    Nat Commun; 2019 Jan; 10(1):390. PubMed ID: 30674886
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.
    Das S; Rai A; Merchant ML; Cave MC; Rai SN
    Genes (Basel); 2021 Dec; 12(12):. PubMed ID: 34946896
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Integrating single-cell transcriptomic data across different conditions, technologies, and species.
    Butler A; Hoffman P; Smibert P; Papalexi E; Satija R
    Nat Biotechnol; 2018 Jun; 36(5):411-420. PubMed ID: 29608179
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.
    Zhang Z; Liu Y; Xiao M; Wang K; Huang Y; Bian J; Yang R; Li F
    Brief Bioinform; 2024 Sep; 25(6):. PubMed ID: 39487083
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
    Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
    PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Clustering scRNA-seq data with the cross-view collaborative information fusion strategy.
    Lou Z; Wei X; Hu Y; Hu S; Wu Y; Tian Z
    Brief Bioinform; 2024 Sep; 25(6):. PubMed ID: 39402696
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Single-cell RNA-seq clustering: datasets, models, and algorithms.
    Peng L; Tian X; Tian G; Xu J; Huang X; Weng Y; Yang J; Zhou L
    RNA Biol; 2020 Jun; 17(6):765-783. PubMed ID: 32116127
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects.
    Sekula M; Gaskins J; Datta S
    Biometrics; 2019 Dec; 75(4):1051-1062. PubMed ID: 31009065
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.