153 related articles for article (PubMed ID: 22988281)
1. Incorporating group correlations in genome-wide association studies using smoothed group Lasso.
Liu J; Huang J; Ma S; Wang K
Biostatistics; 2013 Apr; 14(2):205-19. PubMed ID: 22988281
[TBL] [Abstract][Full Text] [Related]
2. Accounting for linkage disequilibrium in genome-wide association studies: A penalized regression method.
Liu J; Wang K; Ma S; Huang J
Stat Interface; 2013 Jan; 6(1):99-115. PubMed ID: 25258655
[TBL] [Abstract][Full Text] [Related]
3. Combining Sparse Group Lasso and Linear Mixed Model Improves Power to Detect Genetic Variants Underlying Quantitative Traits.
Guo Y; Wu C; Guo M; Zou Q; Liu X; Keinan A
Front Genet; 2019; 10():271. PubMed ID: 31024614
[TBL] [Abstract][Full Text] [Related]
4. Analysis of genome-wide association studies with multiple outcomes using penalization.
Liu J; Huang J; Ma S
PLoS One; 2012; 7(12):e51198. PubMed ID: 23272092
[TBL] [Abstract][Full Text] [Related]
5. Multistage analysis strategies for genome-wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16.
Neuman RJ; Sung YJ
Genet Epidemiol; 2009; 33 Suppl 1(Suppl 1):S19-23. PubMed ID: 19924712
[TBL] [Abstract][Full Text] [Related]
6. Genome-wide association studies for discrete traits.
Thomas DC
Genet Epidemiol; 2009; 33 Suppl 1(Suppl 1):S8-12. PubMed ID: 19924710
[TBL] [Abstract][Full Text] [Related]
7. A permutation approach for selecting the penalty parameter in penalized model selection.
Sabourin JA; Valdar W; Nobel AB
Biometrics; 2015 Dec; 71(4):1185-94. PubMed ID: 26243050
[TBL] [Abstract][Full Text] [Related]
8. SNP selection in genome-wide and candidate gene studies via penalized logistic regression.
Ayers KL; Cordell HJ
Genet Epidemiol; 2010 Dec; 34(8):879-91. PubMed ID: 21104890
[TBL] [Abstract][Full Text] [Related]
9. Resampling-based tests for Lasso in genome-wide association studies.
Arbet J; McGue M; Chatterjee S; Basu S
BMC Genet; 2017 Jul; 18(1):70. PubMed ID: 28738830
[TBL] [Abstract][Full Text] [Related]
10. Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity.
Chu BB; Keys KL; German CA; Zhou H; Zhou JJ; Sobel EM; Sinsheimer JS; Lange K
Gigascience; 2020 Jun; 9(6):. PubMed ID: 32491161
[TBL] [Abstract][Full Text] [Related]
11. Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.
St-Pierre J; Oualkacha K; Bhatnagar SR
Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36708013
[TBL] [Abstract][Full Text] [Related]
12. Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent.
Klosa J; Simon N; Westermark PO; Liebscher V; Wittenburg D
BMC Bioinformatics; 2020 Sep; 21(1):407. PubMed ID: 32933477
[TBL] [Abstract][Full Text] [Related]
13. Penalized regression for genome-wide association screening of sequence data.
Zhou H; Alexander DH; Sehl ME; Sinsheimer JS; Sobel EM; Lange K
Pac Symp Biocomput; 2011; ():106-17. PubMed ID: 21121038
[TBL] [Abstract][Full Text] [Related]
14. SNP selection in genome-wide association studies via penalized support vector machine with MAX test.
Kim J; Sohn I; Kim DD; Jung SH
Comput Math Methods Med; 2013; 2013():340678. PubMed ID: 24174989
[TBL] [Abstract][Full Text] [Related]
15. Genome-wide association studies using a penalized moving-window regression.
Bao M; Wang K
Bioinformatics; 2017 Dec; 33(24):3887-3894. PubMed ID: 28961706
[TBL] [Abstract][Full Text] [Related]
16. Genome-wide association analysis by lasso penalized logistic regression.
Wu TT; Chen YF; Hastie T; Sobel E; Lange K
Bioinformatics; 2009 Mar; 25(6):714-21. PubMed ID: 19176549
[TBL] [Abstract][Full Text] [Related]
17. Iterative hard thresholding for model selection in genome-wide association studies.
Keys KL; Chen GK; Lange K
Genet Epidemiol; 2017 Dec; 41(8):756-768. PubMed ID: 28875524
[TBL] [Abstract][Full Text] [Related]
18. Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models.
Belhechmi S; Bin R; Rotolo F; Michiels S
BMC Bioinformatics; 2020 Jul; 21(1):277. PubMed ID: 32615919
[TBL] [Abstract][Full Text] [Related]
19. Identifying gene-gene interactions using penalized tensor regression.
Wu M; Huang J; Ma S
Stat Med; 2018 Feb; 37(4):598-610. PubMed ID: 29034516
[TBL] [Abstract][Full Text] [Related]
20. Combining least absolute shrinkage and selection operator (LASSO) and principal-components analysis for detection of gene-gene interactions in genome-wide association studies.
D'Angelo GM; Rao D; Gu CC
BMC Proc; 2009 Dec; 3 Suppl 7(Suppl 7):S62. PubMed ID: 20018056
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]