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Items: 1 to 20 of 153

1.
2.

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.

3.
4.

Characterizing genetic interactions in human disease association studies using statistical epistasis networks.

Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH.

BMC Bioinformatics. 2011 Sep 12;12:364. doi: 10.1186/1471-2105-12-364.

5.
6.

A system-level pathway-phenotype association analysis using synthetic feature random forest.

Pan Q, Hu T, Malley JD, Andrew AS, Karagas MR, Moore JH.

Genet Epidemiol. 2014 Apr;38(3):209-19. doi: 10.1002/gepi.21794. Epub 2014 Feb 17.

7.
8.

Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks.

Han B, Chen XW, Talebizadeh Z, Xu H.

BMC Syst Biol. 2012;6 Suppl 3:S14. doi: 10.1186/1752-0509-6-S3-S14. Epub 2012 Dec 17.

9.

AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects.

Zhang Q, Long Q, Ott J.

PLoS Comput Biol. 2014 Jun 5;10(6):e1003627. doi: 10.1371/journal.pcbi.1003627. eCollection 2014 Jun.

10.

Improving strategies for detecting genetic patterns of disease susceptibility in association studies.

Calle ML, Urrea V, Vellalta G, Malats N, Steen KV.

Stat Med. 2008 Dec 30;27(30):6532-46. doi: 10.1002/sim.3431.

PMID:
18837071
11.

ViSEN: methodology and software for visualization of statistical epistasis networks.

Hu T, Chen Y, Kiralis JW, Moore JH.

Genet Epidemiol. 2013 Apr;37(3):283-5. doi: 10.1002/gepi.21718. Epub 2013 Mar 6.

12.

Prioritizing tests of epistasis through hierarchical representation of genomic redundancies.

Cowman T, Koyutürk M.

Nucleic Acids Res. 2017 Aug 21;45(14):e131. doi: 10.1093/nar/gkx505.

13.

Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs.

Lee S, Xing EP.

Bioinformatics. 2012 Jun 15;28(12):i137-46. doi: 10.1093/bioinformatics/bts227.

14.

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.

15.

Cuckoo search epistasis: a new method for exploring significant genetic interactions.

Aflakparast M, Salimi H, Gerami A, Dubé MP, Visweswaran S, Masoudi-Nejad A.

Heredity (Edinb). 2014 Jun;112(6):666-74. doi: 10.1038/hdy.2014.4. Epub 2014 Feb 19.

16.

Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.

Jiang X, Neapolitan RE.

PLoS One. 2012;7(10):e46771. doi: 10.1371/journal.pone.0046771. Epub 2012 Oct 12.

17.

Machine learning approaches for the discovery of gene-gene interactions in disease data.

Upstill-Goddard R, Eccles D, Fliege J, Collins A.

Brief Bioinform. 2013 Mar;14(2):251-60. doi: 10.1093/bib/bbs024. Epub 2012 May 18. Review.

PMID:
22611119
18.

Using biological networks to search for interacting loci in genome-wide association studies.

Emily M, Mailund T, Hein J, Schauser L, Schierup MH.

Eur J Hum Genet. 2009 Oct;17(10):1231-40. doi: 10.1038/ejhg.2009.15. Epub 2009 Mar 11.

19.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit.

Pendergrass SA, Verma SS, Holzinger ER, Moore CB, Wallace J, Dudek SM, Huggins W, Kitchner T, Waudby C, Berg R, McCarty CA, Ritchie MD.

Pac Symp Biocomput. 2013:147-58. Corrected and republished in: Pac Symp Biocomput. 2015;:495-505.

20.

A comment on two-locus epistatic interaction models for genome-wide association studies.

Sohn KA, Wee K.

J Bioinform Comput Biol. 2015 Dec;13(6):1571004. doi: 10.1142/S0219720015710043. Epub 2015 Jul 5.

PMID:
26260855

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