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Items: 1 to 20 of 23

1.

Association of JAK-STAT pathway related genes with lymphoma risk: results of a European case-control study (EpiLymph).

Butterbach K, Beckmann L, de Sanjosé S, Benavente Y, Becker N, Foretova L, Maynadie M, Cocco P, Staines A, Boffetta P, Brennan P, Nieters A.

Br J Haematol. 2011 May;153(3):318-33. doi: 10.1111/j.1365-2141.2011.08632.x. Epub 2011 Mar 21.

PMID:
21418178
2.

Cytokine polymorphisms in Th1/Th2 pathway genes, body mass index, and risk of non-Hodgkin lymphoma.

Chen Y, Zheng T, Lan Q, Foss F, Kim C, Chen X, Dai M, Li Y, Holford T, Leaderer B, Boyle P, Chanock SJ, Rothman N, Zhang Y.

Blood. 2011 Jan 13;117(2):585-90. doi: 10.1182/blood-2010-07-295097. Epub 2010 Oct 15.

3.

Logic Forest: an ensemble classifier for discovering logical combinations of binary markers.

Wolf BJ, Hill EG, Slate EH.

Bioinformatics. 2010 Sep 1;26(17):2183-9. doi: 10.1093/bioinformatics/btq354. Epub 2010 Jul 13.

4.

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.

Schwarz DF, König IR, Ziegler A.

Bioinformatics. 2010 Jul 15;26(14):1752-8. doi: 10.1093/bioinformatics/btq257. Epub 2010 May 26. Erratum in: Bioinformatics. 2011 Feb 1;27(3):439.

5.

Permutation importance: a corrected feature importance measure.

Altmann A, Toloşi L, Sander O, Lengauer T.

Bioinformatics. 2010 May 15;26(10):1340-7. doi: 10.1093/bioinformatics/btq134. Epub 2010 Apr 12.

6.

Maximal conditional chi-square importance in random forests.

Wang M, Chen X, Zhang H.

Bioinformatics. 2010 Mar 15;26(6):831-7. doi: 10.1093/bioinformatics/btq038. Epub 2010 Feb 3.

7.

Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects.

Kim Y, Wojciechowski R, Sung H, Mathias RA, Wang L, Klein AP, Lenroot RK, Malley J, Bailey-Wilson JE.

BMC Proc. 2009 Dec 15;3 Suppl 7:S64.

8.

Machine learning in genome-wide association studies.

Szymczak S, Biernacka JM, Cordell HJ, González-Recio O, König IR, Zhang H, Sun YV.

Genet Epidemiol. 2009;33 Suppl 1:S51-7. doi: 10.1002/gepi.20473.

PMID:
19924717
9.

Detecting gene-gene interactions that underlie human diseases.

Cordell HJ.

Nat Rev Genet. 2009 Jun;10(6):392-404. doi: 10.1038/nrg2579. Review.

10.

Willows: a memory efficient tree and forest construction package.

Zhang H, Wang M, Chen X.

BMC Bioinformatics. 2009 May 5;10:130. doi: 10.1186/1471-2105-10-130.

11.

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. Epub 2009 Mar 8.

12.

A random forest approach to the detection of epistatic interactions in case-control studies.

Jiang R, Tang W, Wu X, Fu W.

BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S65. doi: 10.1186/1471-2105-10-S1-S65.

13.

Identification of SNP interactions using logic regression.

Schwender H, Ickstadt K.

Biostatistics. 2008 Jan;9(1):187-98. Epub 2007 Jun 19.

14.

Bias in random forest variable importance measures: illustrations, sources and a solution.

Strobl C, Boulesteix AL, Zeileis A, Hothorn T.

BMC Bioinformatics. 2007 Jan 25;8:25.

15.

Polymorphisms in immune function genes and risk of non-Hodgkin lymphoma: findings from the New South Wales non-Hodgkin Lymphoma Study.

Purdue MP, Lan Q, Kricker A, Grulich AE, Vajdic CM, Turner J, Whitby D, Chanock S, Rothman N, Armstrong BK.

Carcinogenesis. 2007 Mar;28(3):704-12. Epub 2006 Oct 20.

16.

Common genetic variants in proinflammatory and other immunoregulatory genes and risk for non-Hodgkin lymphoma.

Wang SS, Cerhan JR, Hartge P, Davis S, Cozen W, Severson RK, Chatterjee N, Yeager M, Chanock SJ, Rothman N.

Cancer Res. 2006 Oct 1;66(19):9771-80.

17.

Cytokine polymorphisms in the Th1/Th2 pathway and susceptibility to non-Hodgkin lymphoma.

Lan Q, Zheng T, Rothman N, Zhang Y, Wang SS, Shen M, Berndt SI, Zahm SH, Holford TR, Leaderer B, Yeager M, Welch R, Boyle P, Zhang B, Zou K, Zhu Y, Chanock S.

Blood. 2006 May 15;107(10):4101-8. Epub 2006 Jan 31.

18.

Gene selection and classification of microarray data using random forest.

Díaz-Uriarte R, Alvarez de Andrés S.

BMC Bioinformatics. 2006 Jan 6;7:3.

19.

Screening large-scale association study data: exploiting interactions using random forests.

Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P.

BMC Genet. 2004 Dec 10;5:32.

20.

A simple procedure for estimating the false discovery rate.

Dalmasso C, Broët P, Moreau T.

Bioinformatics. 2005 Mar 1;21(5):660-8. Epub 2004 Oct 12.

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