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Items: 1 to 20 of 94

1.

Identification of cancer fusion drivers using network fusion centrality.

Wu CC, Kannan K, Lin S, Yen L, Milosavljevic A.

Bioinformatics. 2013 May 1;29(9):1174-81. doi: 10.1093/bioinformatics/btt131. Epub 2013 Mar 16.

2.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches.

Latysheva NS, Babu MM.

Nucleic Acids Res. 2016 Jun 2;44(10):4487-503. doi: 10.1093/nar/gkw282. Epub 2016 Apr 21. Review.

3.

Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions.

Shugay M, Ortiz de Mendíbil I, Vizmanos JL, Novo FJ.

Bioinformatics. 2013 Oct 15;29(20):2539-46. doi: 10.1093/bioinformatics/btt445. Epub 2013 Aug 16.

PMID:
23956304
4.

An Efficient Method for Identifying Gene Fusions by Targeted RNA Sequencing from Fresh Frozen and FFPE Samples.

Scolnick JA, Dimon M, Wang IC, Huelga SC, Amorese DA.

PLoS One. 2015 Jul 1;10(7):e0128916. doi: 10.1371/journal.pone.0128916. eCollection 2015.

5.

Identification of fusion genes in breast cancer by paired-end RNA-sequencing.

Edgren H, Murumagi A, Kangaspeska S, Nicorici D, Hongisto V, Kleivi K, Rye IH, Nyberg S, Wolf M, Borresen-Dale AL, Kallioniemi O.

Genome Biol. 2011;12(1):R6. doi: 10.1186/gb-2011-12-1-r6. Epub 2011 Jan 19.

6.

An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer.

Wang XS, Prensner JR, Chen G, Cao Q, Han B, Dhanasekaran SM, Ponnala R, Cao X, Varambally S, Thomas DG, Giordano TJ, Beer DG, Palanisamy N, Sartor MA, Omenn GS, Chinnaiyan AM.

Nat Biotechnol. 2009 Nov;27(11):1005-11. doi: 10.1038/nbt.1584. Epub 2009 Nov 1.

7.

The landscape and therapeutic relevance of cancer-associated transcript fusions.

Yoshihara K, Wang Q, Torres-Garcia W, Zheng S, Vegesna R, Kim H, Verhaak RG.

Oncogene. 2015 Sep 10;34(37):4845-54. doi: 10.1038/onc.2014.406. Epub 2014 Dec 15.

8.

The dJ/dS Ratio Test Reveals Hundreds of Novel Putative Cancer Drivers.

Chen H, Xing K, He X.

Mol Biol Evol. 2015 Aug;32(8):2181-5. doi: 10.1093/molbev/msv083. Epub 2015 Apr 14.

9.

Assessment of protein domain fusions in human protein interaction networks prediction: application to the human kinetochore model.

Morilla I, Lees JG, Reid AJ, Orengo C, Ranea JA.

N Biotechnol. 2010 Dec 31;27(6):755-65. doi: 10.1016/j.nbt.2010.09.005. Epub 2010 Sep 17.

PMID:
20851221
10.

RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method.

Zhao J, Li X, Yao Q, Li M, Zhang J, Ai B, Liu W, Wang Q, Feng C, Liu Y, Bai X, Song C, Li S, Li E, Xu L, Li C.

Oncotarget. 2016 Sep 20;7(38):61054-61068. doi: 10.18632/oncotarget.11064.

11.

Identification of cancer gene fusions based on advanced analysis of the human genome or transcriptome.

Wang L.

Front Med. 2013 Sep;7(3):280-9. doi: 10.1007/s11684-013-0265-3. Epub 2013 Jun 26. Review.

PMID:
23807217
12.

The role of NUP98 gene fusions in hematologic malignancy.

Slape C, Aplan PD.

Leuk Lymphoma. 2004 Jul;45(7):1341-50. Review.

PMID:
15359631
13.

Gene expression complex networks: synthesis, identification, and analysis.

Lopes FM, Cesar RM, Costa Lda F.

J Comput Biol. 2011 Oct;18(10):1353-67. doi: 10.1089/cmb.2010.0118. Epub 2011 May 6.

PMID:
21548810
14.

Predicting interactome network perturbations in human cancer: application to gene fusions in acute lymphoblastic leukemia.

Hajingabo LJ, Daakour S, Martin M, Grausenburger R, Panzer-Grümayer R, Dequiedt F, Simonis N, Twizere JC.

Mol Biol Cell. 2014 Dec 1;25(24):3973-85. doi: 10.1091/mbc.E14-06-1038. Epub 2014 Oct 1.

15.

The emerging complexity of gene fusions in cancer.

Mertens F, Johansson B, Fioretos T, Mitelman F.

Nat Rev Cancer. 2015 Jun;15(6):371-81. doi: 10.1038/nrc3947. Review.

PMID:
25998716
16.

Identification of gene fusion transcripts by transcriptome sequencing in BRCA1-mutated breast cancers and cell lines.

Ha KC, Lalonde E, Li L, Cavallone L, Natrajan R, Lambros MB, Mitsopoulos C, Hakas J, Kozarewa I, Fenwick K, Lord CJ, Ashworth A, Vincent-Salomon A, Basik M, Reis-Filho JS, Majewski J, Foulkes WD.

BMC Med Genomics. 2011 Oct 27;4:75. doi: 10.1186/1755-8794-4-75.

17.

deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data.

McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, Griffith M, Heravi Moussavi A, Senz J, Melnyk N, Pacheco M, Marra MA, Hirst M, Nielsen TO, Sahinalp SC, Huntsman D, Shah SP.

PLoS Comput Biol. 2011 May;7(5):e1001138. doi: 10.1371/journal.pcbi.1001138. Epub 2011 May 19.

18.

Network-based inference framework for identifying cancer genes from gene expression data.

Yang B, Zhang J, Yin Y, Zhang Y.

Biomed Res Int. 2013;2013:401649. doi: 10.1155/2013/401649. Epub 2013 Sep 1.

19.

Cancer systems biology: exploring cancer-associated genes on cellular networks.

Wang E, Lenferink A, O'Connor-McCourt M.

Cell Mol Life Sci. 2007 Jul;64(14):1752-62. Review.

PMID:
17415519
20.

VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

Jia P, Zhao Z.

PLoS Comput Biol. 2014 Feb 6;10(2):e1003460. doi: 10.1371/journal.pcbi.1003460. eCollection 2014 Feb.

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