A Two-Step Penalized Regression Method with Networked Predictors

Statistics in Biosciences

Volumn 4, Issue 1, 2012, Pages 27-46

  Luo, Chong ^a   Pan, Wei ^a   Shen, Xiaotong ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Fused Lasso; Gene networks; Group variable selection; L norm; L norm; Lasso; Microarray gene expression

Indexed keywords

ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; GENE REGULATORY NETWORK; GENETIC ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL GENETICS; MICROARRAY ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; SAMPLE SIZE; SIMULATION; STATISTICAL MODEL;

EID: 84860478382     PISSN: 18671764     EISSN: 18671772     Source Type: Journal    
DOI: 10.1007/s12561-011-9051-4     Document Type: Article

References (20)

1. Binder H, Schumacher M (2008) Comment on "Network-constrained regularization and variable selection for analysis of genomic data". Bioinformatics 24: 2566-2568.
2. Bondell HD, Reich BJ (2008) Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Biometrics 64: 115-123.
3. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32: 407-499.
4. Grant M, Boyd S, Ye Y (2010) CVX: Matlab software for disciplined convex programming. Available at http://www. stanford. edu/boyd/cvx.
5. Higgins ME, Claremont M, Major JE, Sander C, Lash AE (2007) CancerGenes: a gene selection resource for cancer genome projects. Nucleic Acids Res 35 (Suppl 1): D721-D726.
6. Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Shu Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. In: Proceedings of national academy of sciences, vol 103, pp 17402-17407.
7. Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24: 1118-1175.
8. Li C, Li H (2010) Variable selection and regression analysis for graph-structured covariates with an application to genomics. Ann Appl Stat 4: 1498-1516.
9. Meinshausen N (2007) Relaxed Lasso. Comput Stat Data Anal 52: 374-393.
10. Meinshausen N, Bühlmann P (2010) Stability selection (with discussion). J R Stat Soc B 72: 417-473.
11. Pan W, Xie B, Shen X (2010) Incorporating predictor network in penalized regression with application to microarray data. Biometrics 66: 474-484.
12. Shen X, Pan W, Zhu Y (2011) Likelihood-based selection and sharp parameter estimation. To appear in JASA. Available on-line at http://www. sph. umn. edu/biostatistics/research/reports. asp.
13. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc B 58: 267-288.
14. Tibshirani R, Rosset S, Zhu J, Knight K (2005) Sparsity and smoothness via the fused Lasso. J R Stat Soc B 67: 91-108.
15. Wei Z, Li H (2007) A Markov random field model for network-based analysis of genomic data. Bioinformatics 23: 1537-1544.
16. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc B 68: 49-67.
17. Zhao P, Yu B (2004) Boosted Lasso. Tech rep, Dept of Statistics, UC-Berkeley.
18. Zhu Y, Shen X, Pan W (2009) Network-based support vector machine for classification of microarray samples. BMC Bioinform Suppl 10(1): S21.
19. Zhou S (2010) Thresholded Lasso for high dimensional variable selection and statistical estimation. Manuscript.
20. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67: 301-320.