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Items: 1 to 20 of 115

1.

Predicting disease-related subnetworks for type 1 diabetes using a new network activity score.

Gao S, Jia S, Hessner MJ, Wang X.

OMICS. 2012 Oct;16(10):566-78. doi: 10.1089/omi.2012.0029. Epub 2012 Aug 23.

2.

Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM.

Genome Res. 2011 Jul;21(7):1109-21. doi: 10.1101/gr.118992.110. Epub 2011 May 2.

3.

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks.

Erten S, Bebek G, Koyutürk M.

J Comput Biol. 2011 Nov;18(11):1561-74. doi: 10.1089/cmb.2011.0154. Epub 2011 Oct 28.

4.

Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.

Hale PJ, López-Yunez AM, Chen JY.

BMC Syst Biol. 2012;6 Suppl 3:S16. doi: 10.1186/1752-0509-6-S3-S16. Epub 2012 Dec 17.

5.

From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

Wei Z, Wang K, Qu HQ, Zhang H, Bradfield J, Kim C, Frackleton E, Hou C, Glessner JT, Chiavacci R, Stanley C, Monos D, Grant SF, Polychronakos C, Hakonarson H.

PLoS Genet. 2009 Oct;5(10):e1000678. doi: 10.1371/journal.pgen.1000678. Epub 2009 Oct 9.

6.

The type 1 diabetes - HLA susceptibility interactome--identification of HLA genotype-specific disease genes for type 1 diabetes.

Brorsson C, Tue Hansen N, Bergholdt R, Brunak S, Pociot F.

PLoS One. 2010 Mar 5;5(3):e9576. doi: 10.1371/journal.pone.0009576.

7.

Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies.

Hou L, Chen M, Zhang CK, Cho J, Zhao H.

Hum Mol Genet. 2014 May 15;23(10):2780-90. doi: 10.1093/hmg/ddt668. Epub 2013 Dec 30.

8.

Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases.

Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D, Coin L, Levin M.

PLoS One. 2009 Nov 30;4(11):e8068. doi: 10.1371/journal.pone.0008068.

9.
10.

Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.

Mittag F, Büchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, Simón-Sánchez J, Nalls MA, Keller M, Hernandez DG, Gibbs JR, Lesage S, Brice A, Heutink P, Martinez M, Wood NW, Hardy J, Singleton AB, Zell A, Gasser T, Sharma M; International Parkinson’s Disease Genomics Consortium.

Hum Mutat. 2012 Dec;33(12):1708-18. doi: 10.1002/humu.22161. Epub 2012 Aug 3.

PMID:
22777693
11.

Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers.

Tamada Y, Imoto S, Araki H, Nagasaki M, Print C, Charnock-Jones DS, Miyano S.

IEEE/ACM Trans Comput Biol Bioinform. 2011 May-Jun;8(3):683-97. doi: 10.1109/TCBB.2010.68.

PMID:
20714027
12.

DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases.

Zhang W, Chen Y, Sun F, Jiang R.

BMC Syst Biol. 2011 Apr 19;5:55. doi: 10.1186/1752-0509-5-55.

13.

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease.

Talwar P, Silla Y, Grover S, Gupta M, Agarwal R, Kushwaha S, Kukreti R.

BMC Genomics. 2014 Mar 15;15:199. doi: 10.1186/1471-2164-15-199.

14.

Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.

Jia P, Wang L, Fanous AH, Pato CN, Edwards TL; International Schizophrenia Consortium, Zhao Z.

PLoS Comput Biol. 2012;8(7):e1002587. doi: 10.1371/journal.pcbi.1002587. Epub 2012 Jul 5.

15.

Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data.

Brorsson C, Hansen NT, Lage K, Bergholdt R, Brunak S, Pociot F; Diabetes Genetics Consortium.

Diabetes Obes Metab. 2009 Feb;11 Suppl 1:60-6. doi: 10.1111/j.1463-1326.2008.01004.x.

16.

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network.

Linghu B, Snitkin ES, Hu Z, Xia Y, Delisi C.

Genome Biol. 2009;10(9):R91. doi: 10.1186/gb-2009-10-9-r91. Epub 2009 Sep 3.

17.

dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks.

Jia P, Zheng S, Long J, Zheng W, Zhao Z.

Bioinformatics. 2011 Jan 1;27(1):95-102. doi: 10.1093/bioinformatics/btq615. Epub 2010 Nov 2.

18.

Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis.

Duan S, Luo X, Dong C.

Gene. 2013 Dec 1;531(2):347-54. doi: 10.1016/j.gene.2013.08.059. Epub 2013 Aug 29.

PMID:
23994195
19.

Identification of highly synchronized subnetworks from gene expression data.

Gao S, Wang X.

BMC Bioinformatics. 2013;14 Suppl 9:S5. doi: 10.1186/1471-2105-14-S9-S5. Epub 2013 Jun 28.

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