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Items: 1 to 20 of 110

1.

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.

2.

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.

3.

Discovering cancer genes by integrating network and functional properties.

Li L, Zhang K, Lee J, Cordes S, Davis DP, Tang Z.

BMC Med Genomics. 2009 Sep 19;2:61. doi: 10.1186/1755-8794-2-61.

4.

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

Jiang L, Edwards SM, Thomsen B, Workman CT, Guldbrandtsen B, Sørensen P.

BMC Bioinformatics. 2014 Sep 24;15:315. doi: 10.1186/1471-2105-15-315.

5.

Network-based global inference of human disease genes.

Wu X, Jiang R, Zhang MQ, Li S.

Mol Syst Biol. 2008;4:189. doi: 10.1038/msb.2008.27. Epub 2008 May 6.

6.

Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization.

Guney E, Oliva B.

PLoS One. 2012;7(9):e43557. doi: 10.1371/journal.pone.0043557. Epub 2012 Sep 21.

7.

Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

Himmelstein DS, Baranzini SE.

PLoS Comput Biol. 2015 Jul 9;11(7):e1004259. doi: 10.1371/journal.pcbi.1004259. eCollection 2015 Jul.

8.

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.

9.

Degrees of separation as a statistical tool for evaluating candidate genes.

Nelson RM, Pettersson ME.

Comput Biol Med. 2014 Dec;55:49-52. doi: 10.1016/j.compbiomed.2014.10.004. Epub 2014 Oct 14.

PMID:
25450218
10.

Pinpointing disease genes through phenomic and genomic data fusion.

Jiang R, Wu M, Li L.

BMC Genomics. 2015;16 Suppl 2:S3. doi: 10.1186/1471-2164-16-S2-S3. Epub 2015 Jan 21.

11.

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.

12.

Recent approaches to the prioritization of candidate disease genes.

Doncheva NT, Kacprowski T, Albrecht M.

Wiley Interdiscip Rev Syst Biol Med. 2012 Sep-Oct;4(5):429-42. doi: 10.1002/wsbm.1177. Epub 2012 Jun 11. Review.

PMID:
22689539
13.

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
14.

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.

15.

Prioritization of potential candidate disease genes by topological similarity of protein-protein interaction network and phenotype data.

Luo J, Liang S.

J Biomed Inform. 2015 Feb;53:229-36. doi: 10.1016/j.jbi.2014.11.004. Epub 2014 Nov 15.

16.

An integrated genomic analysis of gene-function correlation on schizophrenia susceptibility genes.

Chu TT, Liu Y.

J Hum Genet. 2010 May;55(5):285-92. doi: 10.1038/jhg.2010.24. Epub 2010 Mar 26.

PMID:
20339380
17.

Constructing a gene semantic similarity network for the inference of disease genes.

Jiang R, Gan M, He P.

BMC Syst Biol. 2011;5 Suppl 2:S2. doi: 10.1186/1752-0509-5-S2-S2. Epub 2011 Dec 14.

18.

Inferring gene-phenotype associations via global protein complex network propagation.

Yang P, Li X, Wu M, Kwoh CK, Ng SK.

PLoS One. 2011;6(7):e21502. doi: 10.1371/journal.pone.0021502. Epub 2011 Jul 25.

19.

Laboratory mouse models for the human genome-wide associations.

Kitsios GD, Tangri N, Castaldi PJ, Ioannidis JP.

PLoS One. 2010 Nov 1;5(11):e13782. doi: 10.1371/journal.pone.0013782.

20.

[Genome-wide association study on complex diseases: genetic statistical issues].

Yan WL.

Yi Chuan. 2008 May;30(5):543-9. Review. Chinese.

PMID:
18487142

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