692 related articles for article (PubMed ID: 29364829)
1. 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]
2. An Integrative Approach for Identifying Network Biomarkers of Breast Cancer Subtypes Using Genomic, Interactomic, and Transcriptomic Data.
Firoozbakht F; Rezaeian I; D'agnillo M; Porter L; Rueda L; Ngom A
J Comput Biol; 2017 Aug; 24(8):756-766. PubMed ID: 28650678
[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. Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data.
Nabavi S
BMC Genomics; 2016 Aug; 17(1):638. PubMed ID: 27526849
[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. 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]
7. Identification of ovarian cancer subtype-specific network modules and candidate drivers through an integrative genomics approach.
Zhang D; Chen P; Zheng CH; Xia J
Oncotarget; 2016 Jan; 7(4):4298-309. PubMed ID: 26735889
[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. 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]
10. Identification of Novel Breast Cancer Subtype-Specific Biomarkers by Integrating Genomics Analysis of DNA Copy Number Aberrations and miRNA-mRNA Dual Expression Profiling.
Li D; Xia H; Li ZY; Hua L; Li L
Biomed Res Int; 2015; 2015():746970. PubMed ID: 25961039
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. Integrated analysis of gene expression and copy number identified potential cancer driver genes with amplification-dependent overexpression in 1,454 solid tumors.
Ohshima K; Hatakeyama K; Nagashima T; Watanabe Y; Kanto K; Doi Y; Ide T; Shimoda Y; Tanabe T; Ohnami S; Ohnami S; Serizawa M; Maruyama K; Akiyama Y; Urakami K; Kusuhara M; Mochizuki T; Yamaguchi K
Sci Rep; 2017 Apr; 7(1):641. PubMed ID: 28377632
[TBL] [Abstract][Full Text] [Related]
17. Integrative analysis of cancer driver genes in prostate adenocarcinoma.
Zhao X; Lei Y; Li G; Cheng Y; Yang H; Xie L; Long H; Jiang R
Mol Med Rep; 2019 Apr; 19(4):2707-2715. PubMed ID: 30720096
[TBL] [Abstract][Full Text] [Related]
18. Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis.
Mo W; Ding Y; Zhao S; Zou D; Ding X
PLoS One; 2020; 15(11):e0241924. PubMed ID: 33170908
[TBL] [Abstract][Full Text] [Related]
19. Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules.
Silverbush D; Cristea S; Yanovich-Arad G; Geiger T; Beerenwinkel N; Sharan R
Cell Syst; 2019 May; 8(5):456-466.e5. PubMed ID: 31103572
[TBL] [Abstract][Full Text] [Related]
20. MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice.
Nogales-Cadenas R; Cai Y; Lin JR; Zhang Q; Zhang W; Montagna C; Zhang ZD
Breast Cancer Res; 2016 Jul; 18(1):75. PubMed ID: 27449149
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
