These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
2. 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]
3. Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach. Zhao J; Cheng F; Wang Y; Arteaga CL; Zhao Z Mol Cell Proteomics; 2016 Feb; 15(2):642-56. PubMed ID: 26657081 [TBL] [Abstract][Full Text] [Related]
4. SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering. Van den Eynden J; Fierro AC; Verbeke LP; Marchal K BMC Bioinformatics; 2015 Apr; 16():125. PubMed ID: 25903787 [TBL] [Abstract][Full Text] [Related]
5. An integrative genomics approach for identifying novel functional consequences of PBRM1 truncated mutations in clear cell renal cell carcinoma (ccRCC). Wang Y; Guo X; Bray MJ; Ding Z; Zhao Z BMC Genomics; 2016 Aug; 17 Suppl 7(Suppl 7):515. PubMed ID: 27556922 [TBL] [Abstract][Full Text] [Related]
6. Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis. Zhang Y; Yang W; Li D; Yang JY; Guan R; Yang MQ BMC Med Genomics; 2018 Nov; 11(Suppl 5):104. PubMed ID: 30454048 [TBL] [Abstract][Full Text] [Related]
7. Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types. Fredriksson NJ; Ny L; Nilsson JA; Larsson E Nat Genet; 2014 Dec; 46(12):1258-63. PubMed ID: 25383969 [TBL] [Abstract][Full Text] [Related]
8. 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]
9. Integrating mutation and gene expression cross-sectional data to infer cancer progression. Fleck JL; Pavel AB; Cassandras CG BMC Syst Biol; 2016 Jan; 10():12. PubMed ID: 26810975 [TBL] [Abstract][Full Text] [Related]
10. A pan-cancer analysis of driver gene mutations, DNA methylation and gene expressions reveals that chromatin remodeling is a major mechanism inducing global changes in cancer epigenomes. Youn A; Kim KI; Rabadan R; Tycko B; Shen Y; Wang S BMC Med Genomics; 2018 Nov; 11(1):98. PubMed ID: 30400878 [TBL] [Abstract][Full Text] [Related]
11. Cancer driver mutation prediction through Bayesian integration of multi-omic data. Wang Z; Ng KS; Chen T; Kim TB; Wang F; Shaw K; Scott KL; Meric-Bernstam F; Mills GB; Chen K PLoS One; 2018; 13(5):e0196939. PubMed ID: 29738578 [TBL] [Abstract][Full Text] [Related]
12. Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models. Svetlichnyy D; Imrichova H; Fiers M; Kalender Atak Z; Aerts S PLoS Comput Biol; 2015 Nov; 11(11):e1004590. PubMed ID: 26562774 [TBL] [Abstract][Full Text] [Related]
13. A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types. Cheng F; Liu C; Lin CC; Zhao J; Jia P; Li WH; Zhao Z PLoS Comput Biol; 2015 Sep; 11(9):e1004497. PubMed ID: 26352260 [TBL] [Abstract][Full Text] [Related]
14. 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]