Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 188

1.

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.

2.

gammaMAXT: a fast multiple-testing correction algorithm.

Lishout FV, Gadaleta F, Moore JH, Wehenkel L, Steen KV.

BioData Min. 2015 Nov 20;8:36. doi: 10.1186/s13040-015-0069-x. eCollection 2015.

3.

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.

4.

Enabling personal genomics with an explicit test of epistasis.

Greene CS, Himmelstein DS, Nelson HH, Kelsey KT, Williams SM, Andrew AS, Karagas MR, Moore JH.

Pac Symp Biocomput. 2010:327-36.

5.

mbmdr: an R package for exploring gene-gene interactions associated with binary or quantitative traits.

Calle ML, Urrea V, Malats N, Van Steen K.

Bioinformatics. 2010 Sep 1;26(17):2198-9. doi: 10.1093/bioinformatics/btq352. Epub 2010 Jul 1.

PMID:
20595460
6.

IndOR: a new statistical procedure to test for SNP-SNP epistasis in genome-wide association studies.

Emily M.

Stat Med. 2012 Sep 20;31(21):2359-73. doi: 10.1002/sim.5364. Epub 2012 Jun 18.

PMID:
22711278
7.

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.

8.

Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction.

Mahachie John JM, Cattaert T, Lishout FV, Gusareva ES, Steen KV.

PLoS One. 2012;7(1):e29594. doi: 10.1371/journal.pone.0029594. Epub 2012 Jan 5.

9.

AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies.

Emily M.

Stat Appl Genet Mol Biol. 2016 Apr;15(2):151-71. doi: 10.1515/sagmb-2015-0074.

PMID:
26913459
10.

TEAM: efficient two-locus epistasis tests in human genome-wide association study.

Zhang X, Huang S, Zou F, Wang W.

Bioinformatics. 2010 Jun 15;26(12):i217-27. doi: 10.1093/bioinformatics/btq186.

11.

Haplotype-based quantitative trait mapping using a clustering algorithm.

Li J, Zhou Y, Elston RC.

BMC Bioinformatics. 2006 May 18;7:258.

12.

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.

Mahachie John JM, Van Lishout F, Van Steen K.

Eur J Hum Genet. 2011 Jun;19(6):696-703. doi: 10.1038/ejhg.2011.17. Epub 2011 Mar 16.

13.

A novel approach to detect cumulative genetic effects and genetic interactions in Crohn's disease.

Wang MH, Fiocchi C, Ripke S, Zhu X, Duerr RH, Achkar JP.

Inflamm Bowel Dis. 2013 Aug;19(9):1799-808. doi: 10.1097/MIB.0b013e31828706a0.

14.

Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data.

Liu Y, Maxwell S, Feng T, Zhu X, Elston RC, Koyutürk M, Chance MR.

BMC Syst Biol. 2012;6 Suppl 3:S15. doi: 10.1186/1752-0509-6-S3-S15. Epub 2012 Dec 17.

15.

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.

16.

Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses.

Wongseree W, Assawamakin A, Piroonratana T, Sinsomros S, Limwongse C, Chaiyaratana N.

BMC Bioinformatics. 2009 Sep 17;10:294. doi: 10.1186/1471-2105-10-294.

17.

MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.

Jing PJ, Shen HB.

Bioinformatics. 2015 Mar 1;31(5):634-41. doi: 10.1093/bioinformatics/btu702. Epub 2014 Oct 22.

PMID:
25338719
18.

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.

19.

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.

20.

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.

Supplemental Content

Support Center