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Items: 1 to 20 of 125

1.

BiForce Toolbox: powerful high-throughput computational analysis of gene-gene interactions in genome-wide association studies.

Gyenesei A, Moody J, Laiho A, Semple CA, Haley CS, Wei WH.

Nucleic Acids Res. 2012 Jul;40(Web Server issue):W628-32. doi: 10.1093/nar/gks550. Epub 2012 Jun 11.

2.

High-throughput analysis of epistasis in genome-wide association studies with BiForce.

Gyenesei A, Moody J, Semple CA, Haley CS, Wei WH.

Bioinformatics. 2012 Aug 1;28(15):1957-64. doi: 10.1093/bioinformatics/bts304. Epub 2012 May 21. Erratum in: Bioinformatics. 2013 Oct 15;29(20):2667-8.

3.

Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies.

Ma L, Runesha HB, Dvorkin D, Garbe JR, Da Y.

BMC Bioinformatics. 2008 Jul 21;9:315. doi: 10.1186/1471-2105-9-315.

4.

A whole-genome simulator capable of modeling high-order epistasis for complex disease.

Yang W, Gu CC.

Genet Epidemiol. 2013 Nov;37(7):686-94. doi: 10.1002/gepi.21761. Epub 2013 Oct 1.

5.

SNP-based pathway enrichment analysis for genome-wide association studies.

Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z, Xie X.

BMC Bioinformatics. 2011 Apr 15;12:99. doi: 10.1186/1471-2105-12-99.

6.

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS.

Greene CS, Sinnott-Armstrong NA, Himmelstein DS, Park PJ, Moore JH, Harris BT.

Bioinformatics. 2010 Mar 1;26(5):694-5. doi: 10.1093/bioinformatics/btq009. Epub 2010 Jan 16.

7.

Gene-Gene Interactions Detection Using a Two-stage Model.

Wang Z, Sul JH, Snir S, Lozano JA, Eskin E.

J Comput Biol. 2015 Jun;22(6):563-76. doi: 10.1089/cmb.2014.0163. Epub 2015 Apr 14.

8.

EPIQ-efficient detection of SNP-SNP epistatic interactions for quantitative traits.

Arkin Y, Rahmani E, Kleber ME, Laaksonen R, März W, Halperin E.

Bioinformatics. 2014 Jun 15;30(12):i19-25. doi: 10.1093/bioinformatics/btu261.

9.

FastEpistasis: a high performance computing solution for quantitative trait epistasis.

Schüpbach T, Xenarios I, Bergmann S, Kapur K.

Bioinformatics. 2010 Jun 1;26(11):1468-9. doi: 10.1093/bioinformatics/btq147. Epub 2010 Apr 7.

10.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

Guo X, Meng Y, Yu N, Pan Y.

BMC Bioinformatics. 2014 Apr 10;15:102. doi: 10.1186/1471-2105-15-102.

11.

Properties of local interactions and their potential value in complementing genome-wide association studies.

Wei W, Gyenesei A, Semple CA, Haley CS.

PLoS One. 2013 Aug 5;8(8):e71203. doi: 10.1371/journal.pone.0071203. Print 2013.

12.

Uncovering networks from genome-wide association studies via circular genomic permutation.

Cabrera CP, Navarro P, Huffman JE, Wright AF, Hayward C, Campbell H, Wilson JF, Rudan I, Hastie ND, Vitart V, Haley CS.

G3 (Bethesda). 2012 Sep;2(9):1067-75. doi: 10.1534/g3.112.002618. Epub 2012 Sep 1.

13.

Snat: a SNP annotation tool for bovine by integrating various sources of genomic information.

Jiang J, Jiang L, Zhou B, Fu W, Liu JF, Zhang Q.

BMC Genet. 2011 Oct 7;12:85. doi: 10.1186/1471-2156-12-85.

14.

A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies.

Wang J, Joshi T, Valliyodan B, Shi H, Liang Y, Nguyen HT, Zhang J, Xu D.

BMC Genomics. 2015 Nov 25;16:1011. doi: 10.1186/s12864-015-2217-6.

15.

iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies.

Piriyapongsa J, Ngamphiw C, Intarapanich A, Kulawonganunchai S, Assawamakin A, Bootchai C, Shaw PJ, Tongsima S.

BMC Genomics. 2012;13 Suppl 7:S2. doi: 10.1186/1471-2164-13-S7-S2. Epub 2012 Dec 13.

16.

An efficient algorithm to perform multiple testing in epistasis screening.

Van Lishout F, Mahachie John JM, Gusareva ES, Urrea V, Cleynen I, Théâtre E, Charloteaux B, Calle ML, Wehenkel L, Van Steen K.

BMC Bioinformatics. 2013 Apr 24;14:138. doi: 10.1186/1471-2105-14-138.

17.

Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources.

Ramos EM, Hoffman D, Junkins HA, Maglott D, Phan L, Sherry ST, Feolo M, Hindorff LA.

Eur J Hum Genet. 2014 Jan;22(1):144-7. doi: 10.1038/ejhg.2013.96. Epub 2013 May 22.

18.

Biological knowledge-driven analysis of epistasis in human GWAS with application to lipid traits.

Ma L, Keinan A, Clark AG.

Methods Mol Biol. 2015;1253:35-45. doi: 10.1007/978-1-4939-2155-3_3.

19.

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units.

Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, Wolf C, Ziegler A, Pütz B, Holsboer F, Schölkopf B, Müller-Myhsok B.

Eur J Hum Genet. 2011 Apr;19(4):465-71. doi: 10.1038/ejhg.2010.196. Epub 2010 Dec 8.

20.

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Kam-Thong T, Pütz B, Karbalai N, Müller-Myhsok B, Borgwardt K.

Bioinformatics. 2011 Jul 1;27(13):i214-21. doi: 10.1093/bioinformatics/btr218.

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