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Items: 1 to 20 of 112

1.

PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis.

Ng S, Collisson EA, Sokolov A, Goldstein T, Gonzalez-Perez A, Lopez-Bigas N, Benz C, Haussler D, Stuart JM.

Bioinformatics. 2012 Sep 15;28(18):i640-i646. doi: 10.1093/bioinformatics/bts402.

2.

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:S4. doi: 10.1186/1752-0509-7-S2-S4. Epub 2013 Oct 14.

3.

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.

Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM.

Bioinformatics. 2010 Jun 15;26(12):i237-45. doi: 10.1093/bioinformatics/btq182.

4.

Inferring the paths of somatic evolution in cancer.

Misra N, Szczurek E, Vingron M.

Bioinformatics. 2014 Sep 1;30(17):2456-63. doi: 10.1093/bioinformatics/btu319. Epub 2014 May 7.

PMID:
24812340
5.

Efficient methods for identifying mutated driver pathways in cancer.

Zhao J, Zhang S, Wu LY, Zhang XS.

Bioinformatics. 2012 Nov 15;28(22):2940-7. doi: 10.1093/bioinformatics/bts564. Epub 2012 Sep 14.

PMID:
22982574
6.

Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE).

Paull EO, Carlin DE, Niepel M, Sorger PK, Haussler D, Stuart JM.

Bioinformatics. 2013 Nov 1;29(21):2757-64. doi: 10.1093/bioinformatics/btt471. Epub 2013 Aug 27.

7.

OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes.

Tamborero D, Gonzalez-Perez A, Lopez-Bigas N.

Bioinformatics. 2013 Sep 15;29(18):2238-44. doi: 10.1093/bioinformatics/btt395. Epub 2013 Jul 24.

PMID:
23884480
8.

Domain landscapes of somatic mutations in cancer.

Nehrt NL, Peterson TA, Park D, Kann MG.

BMC Genomics. 2012 Jun 18;13 Suppl 4:S9. doi: 10.1186/1471-2164-13-S4-S9.

9.

A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules.

Zhang S, Li Q, Liu J, Zhou XJ.

Bioinformatics. 2011 Jul 1;27(13):i401-9. doi: 10.1093/bioinformatics/btr206.

10.

Mutation analysis of p53, K-ras, and BRAF genes in colorectal cancer progression.

Calistri D, Rengucci C, Seymour I, Lattuneddu A, Polifemo AM, Monti F, Saragoni L, Amadori D.

J Cell Physiol. 2005 Aug;204(2):484-8.

PMID:
15702478
11.

New approaches to understanding p53 gene tumor mutation spectra.

Hollstein M, Hergenhahn M, Yang Q, Bartsch H, Wang ZQ, Hainaut P.

Mutat Res. 1999 Dec 17;431(2):199-209. Review.

PMID:
10635987
12.

Loss of expression of the p16 tumor suppressor gene is more frequent in advanced ovarian cancers lacking p53 mutations.

Havrilesky LJ, Alvarez AA, Whitaker RS, Marks JR, Berchuck A.

Gynecol Oncol. 2001 Dec;83(3):491-500.

PMID:
11733961
13.

Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM.

Sedgewick AJ, Benz SC, Rabizadeh S, Soon-Shiong P, Vaske CJ.

Bioinformatics. 2013 Jul 1;29(13):i62-70. doi: 10.1093/bioinformatics/btt229.

14.

Exploring the functional landscape of gene expression: directed search of large microarray compendia.

Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG.

Bioinformatics. 2007 Oct 15;23(20):2692-9. Epub 2007 Aug 27.

PMID:
17724061
15.

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 15;31(16):2607-13. doi: 10.1093/bioinformatics/btv164. Epub 2015 Mar 24.

PMID:
25810432
16.

Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data.

Kumar RD, Searleman AC, Swamidass SJ, Griffith OL, Bose R.

Bioinformatics. 2015 Nov 15;31(22):3561-8. doi: 10.1093/bioinformatics/btv430. Epub 2015 Jul 25.

17.

Regulatory motif finding by logic regression.

Keles S, van der Laan MJ, Vulpe C.

Bioinformatics. 2004 Nov 1;20(16):2799-811. Epub 2004 May 27.

PMID:
15166027
19.

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

Seo J, Bakay M, Chen YW, Hilmer S, Shneiderman B, Hoffman EP.

Bioinformatics. 2004 Nov 1;20(16):2534-44. Epub 2004 Apr 29.

PMID:
15117752
20.

The UCSC Cancer Genomics Browser: update 2011.

Sanborn JZ, Benz SC, Craft B, Szeto C, Kober KM, Meyer L, Vaske CJ, Goldman M, Smith KE, Kuhn RM, Karolchik D, Kent WJ, Stuart JM, Haussler D, Zhu J.

Nucleic Acids Res. 2011 Jan;39(Database issue):D951-9. doi: 10.1093/nar/gkq1113. Epub 2010 Nov 8.

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