BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

193 related articles for article (PubMed ID: 28938536)

  • 1. Integrating omics data and protein interaction networks to prioritize driver genes in cancer.
    Zhang T; Zhang D
    Oncotarget; 2017 Aug; 8(35):58050-58060. PubMed ID: 28938536
    [TBL] [Abstract][Full Text] [Related]  

  • 2. LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network.
    Wei PJ; Zhang D; Xia J; Zheng CH
    BMC Bioinformatics; 2016 Dec; 17(Suppl 17):467. PubMed ID: 28155630
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. Sparse overlapping group lasso for integrative multi-omics analysis.
    Park H; Niida A; Miyano S; Imoto S
    J Comput Biol; 2015 Feb; 22(2):73-84. PubMed ID: 25629319
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis.
    Zhang D; Bin Y
    Front Genet; 2020; 11():607798. PubMed ID: 33679866
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.
    Bashashati A; Haffari G; Ding J; Ha G; Lui K; Rosner J; Huntsman DG; Caldas C; Aparicio SA; Shah SP
    Genome Biol; 2012 Dec; 13(12):R124. PubMed ID: 23383675
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Cancer Gene Discovery by Network Analysis of Somatic Mutations Using the MUFFINN Server.
    Han H; Lehner B; Lee I
    Methods Mol Biol; 2019; 1907():37-50. PubMed ID: 30542989
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph.
    Song J; Peng W; Wang F
    BMC Bioinformatics; 2019 May; 20(1):238. PubMed ID: 31088372
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers.
    Zhang Y; Liao G; Bai J; Zhang X; Xu L; Deng C; Yan M; Xie A; Luo T; Long Z; Xiao Y; Li X
    Mol Ther Nucleic Acids; 2019 Sep; 17():362-373. PubMed ID: 31302496
    [TBL] [Abstract][Full Text] [Related]  

  • 14. DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.
    Zhang SW; Xu JY; Zhang T
    Genomics Proteomics Bioinformatics; 2022 Oct; 20(5):928-938. PubMed ID: 36464123
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information.
    Xi J; Wang M; Li A
    Mol Biosyst; 2017 Sep; 13(10):2135-2144. PubMed ID: 28825429
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Integrative omics analysis reveals relationships of genes with synthetic lethal interactions through a pan-cancer analysis.
    Guo L; Li S; Qian B; Wang Y; Duan R; Jiang W; Kang Y; Dou Y; Yang G; Shen L; Wang J; Liang T
    Comput Struct Biotechnol J; 2020; 18():3243-3254. PubMed ID: 33240468
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Identification of ovarian cancer driver genes by using module network integration of multi-omics data.
    Gevaert O; Villalobos V; Sikic BI; Plevritis SK
    Interface Focus; 2013 Aug; 3(4):20130013. PubMed ID: 24511378
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MUFFINN: cancer gene discovery via network analysis of somatic mutation data.
    Cho A; Shim JE; Kim E; Supek F; Lehner B; Lee I
    Genome Biol; 2016 Jun; 17(1):129. PubMed ID: 27333808
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.
    Wei PJ; Zhu AD; Cao R; Zheng C
    Biology (Basel); 2024 Mar; 13(3):. PubMed ID: 38534453
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.