260 related articles for article (PubMed ID: 22982574)
1. Efficient methods for identifying mutated driver pathways in cancer.
Zhao J; Zhang S; Wu LY; Zhang XS
Bioinformatics; 2012 Nov; 28(22):2940-7. PubMed ID: 22982574
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Simulated annealing based algorithm for identifying mutated driver pathways in cancer.
Li HT; Zhang YL; Zheng CH; Wang HQ
Biomed Res Int; 2014; 2014():375980. PubMed ID: 24982873
[TBL] [Abstract][Full Text] [Related]
4. Identification of mutated driver pathways in cancer using a multi-objective optimization model.
Zheng CH; Yang W; Chong YW; Xia JF
Comput Biol Med; 2016 May; 72():22-9. PubMed ID: 26995027
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Snowball: resampling combined with distance-based regression to discover transcriptional consequences of a driver mutation.
Xu Y; Guo X; Sun J; Zhao Z
Bioinformatics; 2015 Jan; 31(1):84-93. PubMed ID: 25192743
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data.
Chen J; Zhang S
Bioinformatics; 2016 Jun; 32(11):1724-32. PubMed ID: 26833341
[TBL] [Abstract][Full Text] [Related]
11. De novo discovery of mutated driver pathways in cancer.
Vandin F; Upfal E; Raphael BJ
Genome Res; 2012 Feb; 22(2):375-85. PubMed ID: 21653252
[TBL] [Abstract][Full Text] [Related]
12. Identification of driver pathways in cancer based on combinatorial patterns of somatic gene mutations.
Li HT; Zhang J; Xia J; Zheng CH
Neoplasma; 2016; 63(1):57-63. PubMed ID: 26639234
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Driver gene mutations based clustering of tumors: methods and applications.
Zhang W; Flemington EK; Zhang K
Bioinformatics; 2018 Jul; 34(13):i404-i411. PubMed ID: 29950003
[TBL] [Abstract][Full Text] [Related]
15. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.
Zou M; Liu Z; Zhang XS; Wang Y
Bioinformatics; 2015 Oct; 31(20):3330-8. PubMed ID: 26092859
[TBL] [Abstract][Full Text] [Related]
16. Identifying mutated driver pathways in cancer by integrating multi-omics data.
Wu J; Cai Q; Wang J; Liao Y
Comput Biol Chem; 2019 Jun; 80():159-167. PubMed ID: 30959272
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. 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]
20. Discovery of co-occurring driver pathways in cancer.
Zhang J; Wu LY; Zhang XS; Zhang S
BMC Bioinformatics; 2014 Aug; 15(1):271. PubMed ID: 25106096
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
