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Items: 1 to 20 of 39

1.

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.

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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
4.

[Current status of SNPs interaction in genome-wide association study].

Li FG, Wang ZP, Hu G, Li H.

Yi Chuan. 2011 Sep;33(9):901-10. Review. Chinese.

PMID:
21951789
5.

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. Review.

PMID:
22611119
6.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Chen CC, Schwender H, Keith J, Nunkesser R, Mengersen K, Macrossan P.

IEEE/ACM Trans Comput Biol Bioinform. 2011 Nov-Dec;8(6):1580-91. doi: 10.1109/TCBB.2011.46. Review.

PMID:
21383421
7.

Bayesian models for detecting epistatic interactions from genetic data.

Zhang Y, Jiang B, Zhu J, Liu JS.

Ann Hum Genet. 2011 Jan;75(1):183-93. doi: 10.1111/j.1469-1809.2010.00621.x. Review.

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[Current status of studies on genome-wide gene-gene interactions].

Shen JW, Hu XH, Shi YY.

Yi Chuan. 2011 Aug;33(8):820-8. Review. Chinese.

PMID:
21831799
10.

Bayesian normalization and identification for differential gene expression data.

Zhang D, Wells MT, Smart CD, Fry WE.

J Comput Biol. 2005 May;12(4):391-406. Review.

PMID:
15882138
11.

A survey about methods dedicated to epistasis detection.

Niel C, Sinoquet C, Dina C, Rocheleau G.

Front Genet. 2015 Sep 10;6:285. doi: 10.3389/fgene.2015.00285. Review.

12.

Bioinformatics challenges for genome-wide association studies.

Moore JH, Asselbergs FW, Williams SM.

Bioinformatics. 2010 Feb 15;26(4):445-55. doi: 10.1093/bioinformatics/btp713. Review.

13.

Epistatic effects of multiple receptor genes on pathophysiology of asthma - its limits and potential for clinical application.

Yoshikawa T, Kanazawa H, Fujimoto S, Hirata K.

Med Sci Monit. 2014 Jan 17;20:64-71. doi: 10.12659/MSM.889754. Review.

14.

Prioritizing GWAS results: A review of statistical methods and recommendations for their application.

Cantor RM, Lange K, Sinsheimer JS.

Am J Hum Genet. 2010 Jan;86(1):6-22. doi: 10.1016/j.ajhg.2009.11.017. Review.

15.

Pathway-based analysis of genomic variation data.

Atias N, Istrail S, Sharan R.

Curr Opin Genet Dev. 2013 Dec;23(6):622-6. doi: 10.1016/j.gde.2013.09.002. Review.

PMID:
24209906
16.

Bayesian systems-based genetic association analysis with effect strength estimation and omic wide interpretation: a case study in rheumatoid arthritis.

Hullám G, Gézsi A, Millinghoffer A, Sárközy P, Bolgár B, Srivastava SK, Pál Z, Buzás EI, Antal P.

Methods Mol Biol. 2014;1142:143-76. doi: 10.1007/978-1-4939-0404-4_14. Review.

PMID:
24706282
17.

An overview of SNP interactions in genome-wide association studies.

Li P, Guo M, Wang C, Liu X, Zou Q.

Brief Funct Genomics. 2015 Mar;14(2):143-55. doi: 10.1093/bfgp/elu036. Review.

PMID:
25241224
18.

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases.

Pattin KA, Moore JH.

Hum Genet. 2008 Aug;124(1):19-29. doi: 10.1007/s00439-008-0522-8. Review.

19.

Probabilistic logic methods and some applications to biology and medicine.

Sakhanenko NA, Galas DJ.

J Comput Biol. 2012 Mar;19(3):316-36. doi: 10.1089/cmb.2011.0234. Review.

PMID:
22401592
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