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Items: 1 to 20 of 102

1.

A multi-omics approach for identifying important pathways and genes in human cancer.

Frost HR, Amos CI.

BMC Bioinformatics. 2018 Dec 12;19(1):479. doi: 10.1186/s12859-018-2476-8.

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:S3. doi: 10.1186/1471-2105-16-S5-S3. Epub 2015 Mar 18.

3.

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.

4.

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.

5.

Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.

Kim D, Li R, Lucas A, Verma SS, Dudek SM, Ritchie MD.

J Am Med Inform Assoc. 2017 May 1;24(3):577-587. doi: 10.1093/jamia/ocw165.

6.

Single cell genomics reveals activation signatures of endogenous SCAR's networks in aneuploid human embryos and clinically intractable malignant tumors.

Glinsky GV.

Cancer Lett. 2016 Oct 10;381(1):176-93. doi: 10.1016/j.canlet.2016.08.001. Epub 2016 Aug 3.

PMID:
27497790
7.

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

Kim D, Joung JG, Sohn KA, Shin H, Park YR, Ritchie MD, Kim JH.

J Am Med Inform Assoc. 2015 Jan;22(1):109-20. doi: 10.1136/amiajnl-2013-002481. Epub 2014 Jul 7.

8.

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

Wang E, Zaman N, Mcgee S, Milanese JS, Masoudi-Nejad A, O'Connor-McCourt M.

Semin Cancer Biol. 2015 Feb;30:4-12. doi: 10.1016/j.semcancer.2014.04.002. Epub 2014 Apr 18. Review.

PMID:
24747696
9.

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.

10.

Integrating mutation and gene expression cross-sectional data to infer cancer progression.

Fleck JL, Pavel AB, Cassandras CG.

BMC Syst Biol. 2016 Jan 25;10:12. doi: 10.1186/s12918-016-0255-6.

11.

Simultaneous identification of multiple driver pathways in cancer.

Leiserson MD, Blokh D, Sharan R, Raphael BJ.

PLoS Comput Biol. 2013;9(5):e1003054. doi: 10.1371/journal.pcbi.1003054. Epub 2013 May 23.

12.

BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways.

Kim I, Choi S, Kim S.

BMC Bioinformatics. 2018 Feb 19;19(Suppl 1):42. doi: 10.1186/s12859-018-2016-6.

13.

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.

14.

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
15.

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.

Kim D, Li R, Dudek SM, Ritchie MD.

J Biomed Inform. 2015 Aug;56:220-8. doi: 10.1016/j.jbi.2015.05.019. Epub 2015 Jun 3.

16.

Computational approaches for the identification of cancer genes and pathways.

Dimitrakopoulos CM, Beerenwinkel N.

Wiley Interdiscip Rev Syst Biol Med. 2017 Jan;9(1). doi: 10.1002/wsbm.1364. Epub 2016 Nov 11. Review.

17.

Somatic Genomics and Clinical Features of Lung Adenocarcinoma: A Retrospective Study.

Shi J, Hua X, Zhu B, Ravichandran S, Wang M, Nguyen C, Brodie SA, Palleschi A, Alloisio M, Pariscenti G, Jones K, Zhou W, Bouk AJ, Boland J, Hicks B, Risch A, Bennett H, Luke BT, Song L, Duan J, Liu P, Kohno T, Chen Q, Meerzaman D, Marconett C, Laird-Offringa I, Mills I, Caporaso NE, Gail MH, Pesatori AC, Consonni D, Bertazzi PA, Chanock SJ, Landi MT.

PLoS Med. 2016 Dec 6;13(12):e1002162. doi: 10.1371/journal.pmed.1002162. eCollection 2016 Dec.

18.

Discovery of mutated subnetworks associated with clinical data in cancer.

Vandin F, Clay P, Upfal E, Raphael BJ.

Pac Symp Biocomput. 2012:55-66.

19.

Identification of druggable cancer driver genes amplified across TCGA datasets.

Chen Y, McGee J, Chen X, Doman TN, Gong X, Zhang Y, Hamm N, Ma X, Higgs RE, Bhagwat SV, Buchanan S, Peng SB, Staschke KA, Yadav V, Yue Y, Kouros-Mehr H.

PLoS One. 2014 May 29;9(5):e98293. doi: 10.1371/journal.pone.0098293. eCollection 2014. Erratum in: PLoS One. 2014;9(9):e107646.

20.

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 24;23(2). pii: E183. doi: 10.3390/molecules23020183.

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