BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

400 related articles for article (PubMed ID: 26099165)

  • 1. A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles.
    Wang L; Li F; Sheng J; Wong ST
    BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S6. PubMed ID: 26099165
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.
    Guo WF; Zhang SW; Feng YH; Liang J; Zeng T; Chen L
    Nucleic Acids Res; 2021 Apr; 49(7):e37. PubMed ID: 33434272
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Discovering personalized driver mutation profiles of single samples in cancer by network control strategy.
    Guo WF; Zhang SW; Liu LL; Liu F; Shi QQ; Zhang L; Tang Y; Zeng T; Chen L
    Bioinformatics; 2018 Jun; 34(11):1893-1903. PubMed ID: 29329368
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era.
    Cheng F; Hong H; Yang S; Wei Y
    Brief Bioinform; 2017 Jul; 18(4):682-697. PubMed ID: 27296652
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. Integrative bioinformatic analyses of an oncogenomic profile reveal the biology of endometrial cancer and guide drug discovery.
    Wong HS; Juan YS; Wu MS; Zhang YF; Hsu YW; Chen HH; Liu WM; Chang WC
    Oncotarget; 2016 Feb; 7(5):5909-23. PubMed ID: 26716509
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Whole-exome sequencing reveals recurrent somatic mutation networks in cancer.
    Liu X; Wang J; Chen L
    Cancer Lett; 2013 Nov; 340(2):270-6. PubMed ID: 23153794
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Network based stratification of major cancers by integrating somatic mutation and gene expression data.
    He Z; Zhang J; Yuan X; Liu Z; Liu B; Tuo S; Liu Y
    PLoS One; 2017; 12(5):e0177662. PubMed ID: 28520777
    [TBL] [Abstract][Full Text] [Related]  

  • 14. MD-Miner: a network-based approach for personalized drug repositioning.
    Wu H; Miller E; Wijegunawardana D; Regan K; Payne PRO; Li F
    BMC Syst Biol; 2017 Oct; 11(Suppl 5):86. PubMed ID: 28984195
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer.
    Liu R; Lv QL; Yu J; Hu L; Zhang LH; Cheng Y; Zhou HH
    Breast Cancer Res Treat; 2015 Jun; 151(3):607-18. PubMed ID: 25981901
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PATIENT-SPECIFIC DATA FUSION FOR CANCER STRATIFICATION AND PERSONALISED TREATMENT.
    Gligorijević V; Malod-Dognin N; Pržulj N
    Pac Symp Biocomput; 2016; 21():321-32. PubMed ID: 26776197
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data.
    Lu X; Lu J; Liao B; Li X; Qian X; Li K
    Sci Rep; 2017 Nov; 7(1):16188. PubMed ID: 29170526
    [TBL] [Abstract][Full Text] [Related]  

  • 19. NETBAGs: a network-based clustering approach with gene signatures for cancer subtyping analysis.
    Wu L; Liu Z; Xu J; Chen M; Fang H; Tong W; Xiao W
    Biomark Med; 2015; 9(11):1053-65. PubMed ID: 26501477
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Construction and analysis of sample-specific driver modules for breast cancer.
    Chen Y; Li H; Sun X
    BMC Genomics; 2022 Oct; 23(1):717. PubMed ID: 36266635
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.