1 |
Alexander, D. and Lange, K. (2011). Stability selection for genome-wide association. Genetic Epidemiology, 35, 722-728.
DOI
|
2 |
Chen, M., Cho, J., and Zhao, H. (2011). Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics, 7, e1001353.
DOI
|
3 |
Du, P., Zhang, X., Huang, C., Jafari, N., Kibbe, W., Hou, L., and Lin, S. (2010). Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics, 11, 587.
DOI
|
4 |
Faraway, J. (2014). Linear Models with R (2nd ed.), Chapman and Hall/CRC.
|
5 |
Friedman J., Hastie T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1-22.
|
6 |
Li, C. and Li, H. (2008). Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics, 24, 1175-1182.
DOI
|
7 |
Peng, J., Wang, P., Zhou, N., and Zhu, J. (2009). Partial correlation estimation by joint sparse regression models. Journal of the American Statistical Association, 104, 735-746.
DOI
|
8 |
Li, C. and Li, H. (2010). Variable selection and regression analysis for graph-structured covariates with an application to genomics. Annals of Applied Statistics, 4, 1498-1516.
DOI
|
9 |
Marsit, C., Christensen, B., Houseman, E., Karagas, M., Wrensch, M., Yeh, R., Nelson, H., Wiemels, J., Zheng, S., Posner, M., McClean, M., Wiencke, J., and Kelsey, K. (2009). Epigenetic profiling reveals etiologically distinct patterns of DNA methylation in head and neck squamous cell carcinoma. Carcinogenesis, 30, 416-422.
DOI
|
10 |
Meinshausen, N. and Buhlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society, Series B, 72, 417-473.
DOI
|
11 |
Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2011). Regularization paths for Cox's proportional hazards model via coordinate descent. Journal of Statistical Software, 39, 1-13.
|
12 |
Sun, H. and Wang, S. (2012). Penalized logistic regression for high-dimensional DNA methylation data with case-control studies. Bioinformatics, 28, 1368-1375.
DOI
|
13 |
Sun, H. and Wang, S. (2013). Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data. Statistics in Medicine, 32, 2127-2139.
DOI
|
14 |
Sun, H., Lin, W., Feng, R., and Li, H. (2014). Network-regularized high-dimensional Cox regression for analysis of genomic data. Statistca Sinica, 24, 1433-1459.
|
15 |
Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67, 301-320.
DOI
|
16 |
Teschendorff, A., Menon, U., Gentry-Maharaj, A., Ramus, S., Weisenberger, D., Shen, H., Campan, M., Noushmehr, H., Bell, C., Maxwell, A., Savage, D., Mueller-Holzner, E., Marth, C., Kocjan, G., Gayther, S., Jones, A., Beck, S., Wagner, W., Laird, P., Jacobs, I., and Widschwendter, M. (2010). Age-dependent DNA methylation of genes that are suppressed in stem cells is hallmark of cancer. Genome Research, 20, 440-446.
DOI
|
17 |
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267-288.
|
18 |
Whittaker, J. (1990). Graphical Models in Applied Mathematical Multivariate Statistics, Wiley, New York.
|