BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

102 related articles for article (PubMed ID: 25411051)

  • 1. A ratiometric-based measure of gene co-expression.
    Abelin AC; Marinov GK; Williams BA; McCue K; Wold BJ
    BMC Bioinformatics; 2014 Nov; 15(1):331. PubMed ID: 25411051
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.
    Shchetynsky K; Diaz-Gallo LM; Folkersen L; Hensvold AH; Catrina AI; Berg L; Klareskog L; Padyukov L
    Arthritis Res Ther; 2017 Feb; 19(1):19. PubMed ID: 28148290
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data.
    Feng J; Meyer CA; Wang Q; Liu JS; Shirley Liu X; Zhang Y
    Bioinformatics; 2012 Nov; 28(21):2782-8. PubMed ID: 22923299
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Exploiting single-cell expression to characterize co-expression replicability.
    Crow M; Paul A; Ballouz S; Huang ZJ; Gillis J
    Genome Biol; 2016 May; 17():101. PubMed ID: 27165153
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Discover hidden splicing variations by mapping personal transcriptomes to personal genomes.
    Stein S; Lu ZX; Bahrami-Samani E; Park JW; Xing Y
    Nucleic Acids Res; 2015 Dec; 43(22):10612-22. PubMed ID: 26578562
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Effective detection of variation in single-cell transcriptomes using MATQ-seq.
    Sheng K; Cao W; Niu Y; Deng Q; Zong C
    Nat Methods; 2017 Mar; 14(3):267-270. PubMed ID: 28092691
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Gene expression and splicing alterations analyzed by high throughput RNA sequencing of chronic lymphocytic leukemia specimens.
    Liao W; Jordaan G; Nham P; Phan RT; Pelegrini M; Sharma S
    BMC Cancer; 2015 Oct; 15():714. PubMed ID: 26474785
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana.
    Giorgi FM; Del Fabbro C; Licausi F
    Bioinformatics; 2013 Mar; 29(6):717-24. PubMed ID: 23376351
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identification of candidate genes for rituximab response in rheumatoid arthritis patients by microarray expression profiling in blood cells.
    Julià A; Barceló M; Erra A; Palacio C; Marsal S
    Pharmacogenomics; 2009 Oct; 10(10):1697-708. PubMed ID: 19842941
    [TBL] [Abstract][Full Text] [Related]  

  • 12. SCALE: modeling allele-specific gene expression by single-cell RNA sequencing.
    Jiang Y; Zhang NR; Li M
    Genome Biol; 2017 Apr; 18(1):74. PubMed ID: 28446220
    [TBL] [Abstract][Full Text] [Related]  

  • 13. EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data.
    Li J; Bushel PR
    BMC Genomics; 2016 Mar; 17():255. PubMed ID: 27004791
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles.
    Budak G; Srivastava R; Janga SC
    RNA; 2017 Jun; 23(6):836-846. PubMed ID: 28336542
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identifying regulatory relationships among genomic loci, biological pathways, and disease.
    Woo JH; Cho SB; Lee E; Kim JH
    Artif Intell Med; 2010 Jul; 49(3):161-5. PubMed ID: 20554166
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Scalable microfluidics for single-cell RNA printing and sequencing.
    Bose S; Wan Z; Carr A; Rizvi AH; Vieira G; Pe'er D; Sims PA
    Genome Biol; 2015 Jun; 16(1):120. PubMed ID: 26047807
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions.
    Qian J; Dolled-Filhart M; Lin J; Yu H; Gerstein M
    J Mol Biol; 2001 Dec; 314(5):1053-66. PubMed ID: 11743722
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Gene expression variability in mammalian embryonic stem cells using single cell RNA-seq data.
    Mantsoki A; Devailly G; Joshi A
    Comput Biol Chem; 2016 Aug; 63():52-61. PubMed ID: 26951854
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Network embedding-based representation learning for single cell RNA-seq data.
    Li X; Chen W; Chen Y; Zhang X; Gu J; Zhang MQ
    Nucleic Acids Res; 2017 Nov; 45(19):e166. PubMed ID: 28977434
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparative evaluation of gene set analysis approaches for RNA-Seq data.
    Rahmatallah Y; Emmert-Streib F; Glazko G
    BMC Bioinformatics; 2014 Dec; 15(1):397. PubMed ID: 25475910
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.