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Items: 1 to 20 of 111

1.

Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction.

Moore JH.

Adv Genet. 2010;72:101-16. doi: 10.1016/B978-0-12-380862-2.00005-9. Review.

PMID:
21029850
2.

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, Barney N, White BC.

J Theor Biol. 2006 Jul 21;241(2):252-61.

PMID:
16457852
3.

Computational analysis of gene-gene interactions using multifactor dimensionality reduction.

Moore JH.

Expert Rev Mol Diagn. 2004 Nov;4(6):795-803. Review.

PMID:
15525222
4.
6.

Pair-wise multifactor dimensionality reduction method to detect gene-gene interactions in a case-control study.

He H, Oetting WS, Brott MJ, Basu S.

Hum Hered. 2010;69(1):60-70. doi: 10.1159/000243155.

PMID:
19797910
7.

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
8.

SVM-based generalized multifactor dimensionality reduction approaches for detecting gene-gene interactions in family studies.

Fang YH, Chiu YF.

Genet Epidemiol. 2012 Feb;36(2):88-98. doi: 10.1002/gepi.21602.

PMID:
22851472
9.

A screening methodology based on Random Forests to improve the detection of gene-gene interactions.

De Lobel L, Geurts P, Baele G, Castro-Giner F, Kogevinas M, Van Steen K.

Eur J Hum Genet. 2010 Oct;18(10):1127-32. doi: 10.1038/ejhg.2010.48.

10.

Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction.

García-Magariños M, López-de-Ullibarri I, Cao R, Salas A.

Ann Hum Genet. 2009 May;73(Pt 3):360-9. doi: 10.1111/j.1469-1809.2009.00511.x.

11.

Epistasis, complexity, and multifactor dimensionality reduction.

Pan Q, Hu T, Moore JH.

Methods Mol Biol. 2013;1019:465-77. doi: 10.1007/978-1-62703-447-0_22. Review.

PMID:
23756906
12.

Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

Hahn LW, Moore JH.

In Silico Biol. 2004;4(2):183-94.

PMID:
15107022
13.

Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases.

Kelemen A, Vasilakos AV, Liang Y.

IEEE Trans Inf Technol Biomed. 2009 Sep;13(5):841-7. doi: 10.1109/TITB.2009.2024144. Review.

PMID:
19556205
14.

[The application of multifactor dimensionality reduction for detecting gene-gene interactions].

Tang X, Li N, Hu YH.

Zhonghua Liu Xing Bing Xue Za Zhi. 2006 May;27(5):437-41. Chinese.

PMID:
16981344
15.

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.

16.

A support vector machine approach for detecting gene-gene interaction.

Chen SH, Sun J, Dimitrov L, Turner AR, Adams TS, Meyers DA, Chang BL, Zheng SL, Grönberg H, Xu J, Hsu FC.

Genet Epidemiol. 2008 Feb;32(2):152-67.

PMID:
17968988
17.

Neural networks for modeling gene-gene interactions in association studies.

Günther F, Wawro N, Bammann K.

BMC Genet. 2009 Dec 23;10:87. doi: 10.1186/1471-2156-10-87.

19.

MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.

Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W.

BMC Bioinformatics. 2009 Jan 9;10:13. doi: 10.1186/1471-2105-10-13.

20.

A novel method to identify gene-gene effects in nuclear families: the MDR-PDT.

Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH.

Genet Epidemiol. 2006 Feb;30(2):111-23.

PMID:
16374833
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