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Results: 1 to 20 of 163

Similar articles for PubMed (Select 22863359)

1.

Fast accurate missing SNP genotype local imputation.

Wang Y, Cai Z, Stothard P, Moore S, Goebel R, Wang L, Lin G.

BMC Res Notes. 2012 Aug 3;5:404. doi: 10.1186/1756-0500-5-404.

2.

Improving prevalence estimation through data fusion: methods and validation.

Aluja-Banet T, Daunis-I-Estadella J, Brunsó N, Mompart-Penina A.

BMC Med Inform Decis Mak. 2015 Jun 24;15:49. doi: 10.1186/s12911-015-0169-z.

3.

Improved Ancestry Estimation for both Genotyping and Sequencing Data using Projection Procrustes Analysis and Genotype Imputation.

Wang C, Zhan X, Liang L, Abecasis GR, Lin X.

Am J Hum Genet. 2015 Jun 4;96(6):926-37. doi: 10.1016/j.ajhg.2015.04.018. Epub 2015 May 28.

PMID:
26027497
4.

Impact of missing data imputation methods on gene expression clustering and classification.

de Souto MC, Jaskowiak PA, Costa IG.

BMC Bioinformatics. 2015 Feb 26;16:64. doi: 10.1186/s12859-015-0494-3.

5.

Traffic speed data imputation method based on tensor completion.

Ran B, Tan H, Feng J, Liu Y, Wang W.

Comput Intell Neurosci. 2015;2015:364089. doi: 10.1155/2015/364089. Epub 2015 Mar 3.

6.

Accounting for dependence induced by weighted KNN imputation in paired samples, motivated by a colorectal cancer study.

Suyundikov A, Stevens JR, Corcoran C, Herrick J, Wolff RK, Slattery ML.

PLoS One. 2015 Apr 7;10(4):e0119876. doi: 10.1371/journal.pone.0119876. eCollection 2015.

7.

How imputation errors bias genomic predictions.

Pimentel EC, Edel C, Emmerling R, Götz KU.

J Dairy Sci. 2015 Jun;98(6):4131-8. doi: 10.3168/jds.2014-9170. Epub 2015 Apr 1.

8.

Effect of reference population size and available ancestor genotypes on imputation of Mexican Holstein genotypes.

García-Ruiz A, Ruiz-Lopez FJ, Wiggans GR, Van Tassell CP, Montaldo HH.

J Dairy Sci. 2015 May;98(5):3478-84. doi: 10.3168/jds.2014-9132. Epub 2015 Mar 12.

PMID:
25771055
9.

Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass.

Ramstein GP, Lipka AE, Lu F, Costich DE, Cherney JH, Buckler ES, Casler MD.

G3 (Bethesda). 2015 Mar 12;5(5):891-909. doi: 10.1534/g3.115.017533.

10.

Second-generation PLINK: rising to the challenge of larger and richer datasets.

Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ.

Gigascience. 2015 Feb 25;4:7. doi: 10.1186/s13742-015-0047-8. eCollection 2015.

11.

SNP imputation bias reduces effect size determination.

Khankhanian P, Din L, Caillier SJ, Gourraud PA, Baranzini SE.

Front Genet. 2015 Feb 9;6:30. doi: 10.3389/fgene.2015.00030. eCollection 2015.

12.

Variable selection in the presence of missing data: resampling and imputation.

Long Q, Johnson BA.

Biostatistics. 2015 Jul;16(3):596-610. doi: 10.1093/biostatistics/kxv003. Epub 2015 Feb 18.

PMID:
25694614
13.

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel.

Delaneau O, Marchini J; 1000 Genomes Project Consortium; 1000 Genomes Project Consortium.

Nat Commun. 2014 Jun 13;5:3934. doi: 10.1038/ncomms4934.

14.

Interaction association analysis of imputed SNPs in case-control and follow-up studies.

Subirana I, González JR.

Genet Epidemiol. 2015 Mar;39(3):185-96. doi: 10.1002/gepi.21883. Epub 2015 Jan 22.

PMID:
25613387
15.

COIL: a methodology for evaluating malarial complexity of infection using likelihood from single nucleotide polymorphism data.

Galinsky K, Valim C, Salmier A, de Thoisy B, Musset L, Legrand E, Faust A, Baniecki ML, Ndiaye D, Daniels RF, Hartl DL, Sabeti PC, Wirth DF, Volkman SK, Neafsey DE.

Malar J. 2015 Jan 19;14(1):4. [Epub ahead of print]

16.

Imputation of Truncated p-Values For Meta-Analysis Methods and Its Genomic Application.

Tang S, Ding Y, Sibille E, Mogil J, Lariviere WR, Tseng GC.

Ann Appl Stat. 2014 Dec;8(4):2150-2174.

17.

Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.

Rogers A, Beck A, Tintle NL.

BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S22. doi: 10.1186/1753-6561-8-S1-S22. eCollection 2014.

18.

Joint analysis of sequence data and single-nucleotide polymorphism data using pedigree information for imputation and recombination inference.

Song S, Shields R, Li X, Li J.

BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S20. doi: 10.1186/1753-6561-8-S1-S20. eCollection 2014.

19.

Genotypic discrepancies arising from imputation.

Hinrichs AL, Culverhouse RC, Suarez BK.

BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S17. doi: 10.1186/1753-6561-8-S1-S17. eCollection 2014.

20.

Analysis of the progression of systolic blood pressure using imputation of missing phenotype values.

Vaitsiakhovich T, Drichel D, Angisch M, Becker T, Herold C, Lacour A.

BMC Proc. 2014 Jun 17;8(Suppl 1):S83. doi: 10.1186/1753-6561-8-S1-S83. eCollection 2014.

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