416 related articles for article (PubMed ID: 27426053)
1. 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]
2. 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]
3. 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]
4. 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]
5. Detection of driver pathways using mutated gene network in cancer.
Li F; Gao L; Ma X; Yang X
Mol Biosyst; 2016 Jun; 12(7):2135-41. PubMed ID: 27118146
[TBL] [Abstract][Full Text] [Related]
6. KatzDriver: A network based method to cancer causal genes discovery in gene regulatory network.
Akhavan-Safar M; Teimourpour B
Biosystems; 2021 Mar; 201():104326. PubMed ID: 33309969
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency.
Bokhari Y; Alhareeri A; Arodz T
BMC Bioinformatics; 2020 Mar; 21(1):122. PubMed ID: 32293263
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms.
Zhang J; Zhang S
IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(3):988-998. PubMed ID: 28113329
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis.
Pulido-Tamayo S; Weytjens B; De Maeyer D; Marchal K
Sci Rep; 2016 Nov; 6():36257. PubMed ID: 27808240
[TBL] [Abstract][Full Text] [Related]
17. Machine Learning Classification and Structure-Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes.
Agajanian S; Odeyemi O; Bischoff N; Ratra S; Verkhivker GM
J Chem Inf Model; 2018 Oct; 58(10):2131-2150. PubMed ID: 30253099
[TBL] [Abstract][Full Text] [Related]
18. Comprehensive evaluation of computational methods for predicting cancer driver genes.
Shi X; Teng H; Shi L; Bi W; Wei W; Mao F; Sun Z
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35037014
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A compendium of mutational cancer driver genes.
Martínez-Jiménez F; Muiños F; Sentís I; Deu-Pons J; Reyes-Salazar I; Arnedo-Pac C; Mularoni L; Pich O; Bonet J; Kranas H; Gonzalez-Perez A; Lopez-Bigas N
Nat Rev Cancer; 2020 Oct; 20(10):555-572. PubMed ID: 32778778
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]