BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

383 related articles for article (PubMed ID: 31115714)

  • 1. A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration.
    Zhang W; Wang SL
    Biochem Genet; 2020 Feb; 58(1):16-39. PubMed ID: 31115714
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis.
    Li A; Chapuy B; Varelas X; Sebastiani P; Monti S
    Sci Rep; 2019 Nov; 9(1):16904. PubMed ID: 31729402
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Discovering potential cancer driver genes by an integrated network-based approach.
    Shi K; Gao L; Wang B
    Mol Biosyst; 2016 Aug; 12(9):2921-31. PubMed ID: 27426053
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method.
    Amgalan B; Lee H
    Bioinformatics; 2015 Aug; 31(15):2452-60. PubMed ID: 25819079
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.
    Merid SK; Goranskaya D; Alexeyenko A
    BMC Bioinformatics; 2014 Sep; 15(1):308. PubMed ID: 25236784
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identifying overlapping mutated driver pathways by constructing gene networks in cancer.
    Wu H; Gao L; Li F; Song F; Yang X; Kasabov N
    BMC Bioinformatics; 2015; 16 Suppl 5(Suppl 5):S3. PubMed ID: 25859819
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Identification of druggable cancer driver genes amplified across TCGA datasets.
    Chen Y; McGee J; Chen X; Doman TN; Gong X; Zhang Y; Hamm N; Ma X; Higgs RE; Bhagwat SV; Buchanan S; Peng SB; Staschke KA; Yadav V; Yue Y; Kouros-Mehr H
    PLoS One; 2014; 9(5):e98293. PubMed ID: 24874471
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Inferring causal genomic alterations in breast cancer using gene expression data.
    Tran LM; Zhang B; Zhang Z; Zhang C; Xie T; Lamb JR; Dai H; Schadt EE; Zhu J
    BMC Syst Biol; 2011 Aug; 5():121. PubMed ID: 21806811
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Bayesian variable selection with graphical structure learning: Applications in integrative genomics.
    Kundu S; Cheng Y; Shin M; Manyam G; Mallick BK; Baladandayuthapani V
    PLoS One; 2018; 13(7):e0195070. PubMed ID: 30059495
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of driver copy number alterations in diverse cancer types and application in drug repositioning.
    Zhou W; Zhao Z; Wang R; Han Y; Wang C; Yang F; Han Y; Liang H; Qi L; Wang C; Guo Z; Gu Y
    Mol Oncol; 2017 Oct; 11(10):1459-1474. PubMed ID: 28719033
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level.
    Park H; Niida A; Imoto S; Miyano S
    J Comput Biol; 2017 Feb; 24(2):138-152. PubMed ID: 27759426
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer.
    Silva GO; He X; Parker JS; Gatza ML; Carey LA; Hou JP; Moulder SL; Marcom PK; Ma J; Rosen JM; Perou CM
    Breast Cancer Res Treat; 2015 Jul; 152(2):347-56. PubMed ID: 26109346
    [TBL] [Abstract][Full Text] [Related]  

  • 16. ProcessDriver: A computational pipeline to identify copy number drivers and associated disrupted biological processes in cancer.
    Baur B; Bozdag S
    Genomics; 2017 Jul; 109(3-4):233-240. PubMed ID: 28438487
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The Integrated Analyses of Driver Genes Identify Key Biomarkers in Thyroid Cancer.
    Xu Q; Song A; Xie Q
    Technol Cancer Res Treat; 2020; 19():1533033820940440. PubMed ID: 32812852
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework.
    Yang H; Wei Q; Zhong X; Yang H; Li B
    Bioinformatics; 2017 Feb; 33(4):483-490. PubMed ID: 27797769
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Combined analysis of gene expression, DNA copy number, and mutation profiling data to display biological process anomalies in individual breast cancers.
    Shi W; Balazs B; Györffy B; Jiang T; Symmans WF; Hatzis C; Pusztai L
    Breast Cancer Res Treat; 2014 Apr; 144(3):561-8. PubMed ID: 24619174
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel network control model for identifying personalized driver genes in cancer.
    Guo WF; Zhang SW; Zeng T; Li Y; Gao J; Chen L
    PLoS Comput Biol; 2019 Nov; 15(11):e1007520. PubMed ID: 31765387
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.