BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

560 related articles for article (PubMed ID: 26810975)

  • 1. Integrating mutation and gene expression cross-sectional data to infer cancer progression.
    Fleck JL; Pavel AB; Cassandras CG
    BMC Syst Biol; 2016 Jan; 10():12. PubMed ID: 26810975
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Simultaneous inference of cancer pathways and tumor progression from cross-sectional mutation data.
    Raphael BJ; Vandin F
    J Comput Biol; 2015 Jun; 22(6):510-27. PubMed ID: 25785493
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data.
    Zhang J; Zhang S; Wang Y; Zhang XS
    BMC Syst Biol; 2013; 7 Suppl 2(Suppl 2):S4. PubMed ID: 24565034
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.
    Pavel AB; Sonkin D; Reddy A
    BMC Syst Biol; 2016 Feb; 10():16. PubMed ID: 26864072
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
    Suo C; Hrydziuszko O; Lee D; Pramana S; Saputra D; Joshi H; Calza S; Pawitan Y
    Bioinformatics; 2015 Aug; 31(16):2607-13. PubMed ID: 25810432
    [TBL] [Abstract][Full Text] [Related]  

  • 6. TP53 mutations, expression and interaction networks in human cancers.
    Wang X; Sun Q
    Oncotarget; 2017 Jan; 8(1):624-643. PubMed ID: 27880943
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Frequent mutations in acetylation and ubiquitination sites suggest novel driver mechanisms of cancer.
    Narayan S; Bader GD; Reimand J
    Genome Med; 2016 May; 8(1):55. PubMed ID: 27175787
    [TBL] [Abstract][Full Text] [Related]  

  • 8. HSP27 expression in primary colorectal cancers is dependent on mutation of KRAS and PI3K/AKT activation status and is independent of TP53.
    Ghosh A; Lai C; McDonald S; Suraweera N; Sengupta N; Propper D; Dorudi S; Silver A
    Exp Mol Pathol; 2013 Feb; 94(1):103-8. PubMed ID: 22982087
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Mutational characterization of individual breast tumors: TP53 and PI3K pathway genes are frequently and distinctively mutated in different subtypes.
    Boyault S; Drouet Y; Navarro C; Bachelot T; Lasset C; Treilleux I; Tabone E; Puisieux A; Wang Q
    Breast Cancer Res Treat; 2012 Feb; 132(1):29-39. PubMed ID: 21512767
    [TBL] [Abstract][Full Text] [Related]  

  • 10. ZDOG: zooming in on dominating genes with mutations in cancer pathways.
    Alberts R; Chen J; Zhang L
    BMC Bioinformatics; 2019 Dec; 20(1):740. PubMed ID: 31888434
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identifying cancer type specific oncogenes and tumor suppressors using limited size data.
    Pavel AB; Vasile CI
    J Bioinform Comput Biol; 2016 Dec; 14(6):1650031. PubMed ID: 27712196
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Somatic mutation and gain of copy number of PIK3CA in human breast cancer.
    Wu G; Xing M; Mambo E; Huang X; Liu J; Guo Z; Chatterjee A; Goldenberg D; Gollin SM; Sukumar S; Trink B; Sidransky D
    Breast Cancer Res; 2005; 7(5):R609-16. PubMed ID: 16168105
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.
    Verbeke LP; Van den Eynden J; Fierro AC; Demeester P; Fostier J; Marchal K
    PLoS One; 2015; 10(7):e0133503. PubMed ID: 26217958
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients.
    Zhang J; Abrams Z; Parvin JD; Huang K
    BMC Genomics; 2016 Aug; 17 Suppl 7(Suppl 7):513. PubMed ID: 27556157
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Discovery of mutated subnetworks associated with clinical data in cancer.
    Vandin F; Clay P; Upfal E; Raphael BJ
    Pac Symp Biocomput; 2012; ():55-66. PubMed ID: 22174262
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.
    Lu X; Li X; Liu P; Qian X; Miao Q; Peng S
    Molecules; 2018 Jan; 23(2):. PubMed ID: 29364829
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis.
    Zhang Y; Yang W; Li D; Yang JY; Guan R; Yang MQ
    BMC Med Genomics; 2018 Nov; 11(Suppl 5):104. PubMed ID: 30454048
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
    Kim D; Li R; Dudek SM; Ritchie MD
    J Biomed Inform; 2015 Aug; 56():220-8. PubMed ID: 26048077
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of constrained cancer driver genes based on mutation timing.
    Sakoparnig T; Fried P; Beerenwinkel N
    PLoS Comput Biol; 2015 Jan; 11(1):e1004027. PubMed ID: 25569148
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Analysis of PIK3CA Mutations and Activation Pathways in Triple Negative Breast Cancer.
    Cossu-Rocca P; OrrĂ¹ S; Muroni MR; Sanges F; Sotgiu G; Ena S; Pira G; Murgia L; Manca A; Uras MG; Sarobba MG; Urru S; De Miglio MR
    PLoS One; 2015; 10(11):e0141763. PubMed ID: 26540293
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 28.