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Items: 1 to 20 of 161

1.

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.

2.

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers.

Hwang TH, Atluri G, Kuang R, Kumar V, Starr T, Silverstein KA, Haverty PM, Zhang Z, Liu J.

BMC Genomics. 2013 Jul 3;14:440. doi: 10.1186/1471-2164-14-440.

3.

Identifying causal genes and dysregulated pathways in complex diseases.

Kim YA, Wuchty S, Przytycka TM.

PLoS Comput Biol. 2011 Mar;7(3):e1001095. doi: 10.1371/journal.pcbi.1001095. Epub 2011 Mar 3.

4.

DINGO: differential network analysis in genomics.

Ha MJ, Baladandayuthapani V, Do KA.

Bioinformatics. 2015 Nov 1;31(21):3413-20. doi: 10.1093/bioinformatics/btv406. Epub 2015 Jul 6.

5.

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 15;33(4):483-490. doi: 10.1093/bioinformatics/btw662.

PMID:
27797769
6.

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.

7.

ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

Shen Y, Rahman M, Piccolo SR, Gusenleitner D, El-Chaar NN, Cheng L, Monti S, Bild AH, Johnson WE.

Bioinformatics. 2015 Jun 1;31(11):1745-53. doi: 10.1093/bioinformatics/btv031. Epub 2015 Jan 22.

8.

Discovering gene-environment interactions in glioblastoma through a comprehensive data integration bioinformatics method.

Kunkle B, Yoo C, Roy D.

Neurotoxicology. 2013 Mar;35:1-14. doi: 10.1016/j.neuro.2012.11.001. Epub 2012 Dec 20.

PMID:
23261424
9.

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

Durmaz A, Henderson TAD, Brubaker D, Bebek G.

Pac Symp Biocomput. 2017;22:402-413. doi: 10.1142/9789813207813_0038.

10.

integIRTy: a method to identify genes altered in cancer by accounting for multiple mechanisms of regulation using item response theory.

Tong P, Coombes KR.

Bioinformatics. 2012 Nov 15;28(22):2861-9. doi: 10.1093/bioinformatics/bts561. Epub 2012 Sep 26.

11.

A systematic comparison of copy number alterations in four types of female cancer.

Kaveh F, Baumbusch LO, Nebdal D, Børresen-Dale AL, Lingjærde OC, Edvardsen H, Kristensen VN, Solvang HK.

BMC Cancer. 2016 Nov 22;16(1):913. doi: 10.1186/s12885-016-2899-4. Erratum in: BMC Cancer. 2018 Jan 16;18(1):80.

12.

Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.

Li W, Zhang S, Liu CC, Zhou XJ.

Bioinformatics. 2012 Oct 1;28(19):2458-66. Epub 2012 Aug 3.

13.

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.

14.

PINCAGE: probabilistic integration of cancer genomics data for perturbed gene identification and sample classification.

Świtnicki MP, Juul M, Madsen T, Sørensen KD, Pedersen JS.

Bioinformatics. 2016 May 1;32(9):1353-65. doi: 10.1093/bioinformatics/btv758. Epub 2016 Jan 6.

PMID:
26740525
15.

Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.

Verbeke LP, Van den Eynden J, Fierro AC, Demeester P, Fostier J, Marchal K.

PLoS One. 2015 Jul 28;10(7):e0133503. doi: 10.1371/journal.pone.0133503. eCollection 2015.

16.

Integrative gene set analysis of multi-platform data with sample heterogeneity.

Hu J, Tzeng JY.

Bioinformatics. 2014 Jun 1;30(11):1501-7. doi: 10.1093/bioinformatics/btu060. Epub 2014 Jan 30.

17.

MIRAGAA--a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer.

Gaire RK, Bailey J, Bearfoot J, Campbell IG, Stuckey PJ, Haviv I.

Bioinformatics. 2010 Jan 15;26(2):161-7. doi: 10.1093/bioinformatics/btp654. Epub 2009 Nov 23.

PMID:
19933823
18.

An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.

Chari R, Coe BP, Vucic EA, Lockwood WW, Lam WL.

BMC Syst Biol. 2010 May 17;4:67. doi: 10.1186/1752-0509-4-67.

19.

IndividualizedPath: identifying genetic alterations contributing to the dysfunctional pathways in glioblastoma individuals.

Ping Y, Zhang H, Deng Y, Wang L, Zhao H, Pang L, Fan H, Xu C, Li F, Zhang Y, Gong Y, Xiao Y, Li X.

Mol Biosyst. 2014 Aug;10(8):2031-42. doi: 10.1039/c4mb00289j.

PMID:
24911613
20.

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

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