Optimized application of penalized regression methods to diverse genomic data

Bioinformatics

Volumn 27, Issue 24, 2011, Pages 3399-3406

  Waldron, Levi ^a,b   Pintilie, Melania ^b   Tsao, Ming Sound ^b   Shepherd, Frances A ^b   Huttenhower, Curtis ^a   Jurisica, Igor ^b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY HEALTH NETWORK   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BACTERIUM; BIOLOGY; CLASSIFICATION; COMPUTER PROGRAM; COMPUTER SIMULATION; FECES; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METAGENOMICS; METHODOLOGY; MICROBIOLOGY; MORTALITY; NEOPLASM; OBESITY; REGRESSION ANALYSIS;

BACTERIA; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; FECES; GENE EXPRESSION PROFILING; HUMANS; METAGENOMICS; NEOPLASMS; OBESITY; REGRESSION ANALYSIS; SOFTWARE;

EID: 83355174923     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr591     Document Type: Article

References (40)

1. Beer, D.G. et al. (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med., 8, 816-824.
2. Boulesteix, A.L. (2006) Reader's reaction to "Dimension reduction for classification with gene expression microarray data" by Dai et al (2006). Stat. Appl. Genet. Mol. Biol., 5, Article16.
3. Bøvelstad, H.M. et al. (2007) Predicting survival from microarray data - a comparative study. Bioinformatics, 23, 2080-2087.
4. Breiman, L. (2001) Random Forests. Mach. Learn., 45, 5-32.
5. Bühlmann, P. (2007) Boosting algorithms: regularization, prediction and model fitting. Stat. Sci., 22, 477-505.
6. Byrd, R.H. et al. (1995)Alimited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput., 16, 1190-1208.
7. Chen, H.-Y. et al. (2007) A five-gene signature and clinical outcome in non-small-cell lung cancer. N. Engl. J. Med., 356, 11-20.
8. Cox, D.R. (1972) Regression models and life-tables. J. R. Stat. Soc. Ser. B, 34, 187-220.
9. Fan, J. and Lv, J. (2008) Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. Ser. B, 70, 849-911.
10. Goeman, J.J. (2010) L1 penalized estimation in the Cox proportional hazards model. Biometr. J. Biometri. Zeitsch., 52, 70-84.
11. Gui, J. and Li, H. (2005) Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics, 21, 3001-3008.
12. Guyon, I. et al. (2010) Model selection: beyond the Bayesian/frequentist divide. J.Mach. Learn. Res., 11, 61-87.
13. Harrell, F.E. (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New York.
14. Harrell, F.E. Jr et al. (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med., 15, 361-387.
15. Hesterberg, T. (2008) Least angle and θ{symbol}1 penalized regression: a review. Stat. Surv., 2, 61-93.
16. Hoerl, A.E. and Kennard, R.W. (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.
17. Hosmer, D.W. and Lemeshow, S. (1999) Applied survival analysis: regression modeling of time to event data. J. Wiley, New York.
18. Jelizarow, M. et al. (2010) Over-optimism in bioinformatics: an illustration. Bioinformatics, 26, 1990-1998.
19. Ley, R.E. et al. (2006) Microbial ecology: human gut microbes associated with obesity. Nature, 444, 1022-1023.
20. Michiels, S. et al. (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet, 365, 488-492.
21. Molinaro, A.M. et al. (2005) Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21, 3301-3307.
22. Qin, J. et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 464, 59-65.
23. R Development Core Team. (2010) R: A Language and Environment for Statistical Computing. Vienna, Austria.
24. Sharma, R. et al. (2008) Systemic inflammatory response predicts prognosis in patients with advanced-stage colorectal cancer. Clin. Colorectal Cancer, 7, 331-337.
25. Shedden, K. et al. (2008) Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat. Med., 14, 822-827.
26. Simon, R.M. (2003) Design and analysis of DNA microarray investigations. Springer, New York.
27. Simon, R.M. et al. (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief. Bioinformatics, 12, 203-214.
28. Sørlie, T. et al. (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA, 98, 10869-10874.
29. Subramanian, A. et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA, 102, 15545-15550.
30. Tibshirani, R. (1996) Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B, 58, 267-288.
31. Tibshirani, R. (1997) The lasso method for variable selection in the Cox model. Stat. Med., 16, 385-395.
32. Tibshirani, R.J. (2009) Univariate shrinkage in the Cox model for high dimensional data. Stat. Appl. Genet. Mol. Biol., 8, 21-21.
33. Turnbaugh, P.J. et al. (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature, 444, 1027-1131.
34. van de Vijver, M.J. et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med., 347, 1999-2009.
35. Venables, W.N. and Ripley, B.D. (2002) Modern Applied Statistics with S. Springer, New York.
36. Verweij, P.J. and Van Houwelingen, H.C. (1993) Cross-validation in survival analysis. Stat. Med., 12, 2305-2314.
37. Verweij, P.J. and Van Houwelingen, H.C. (1994) Penalized likelihood in Cox regression. Stat. Med., 13, 2427-2436.
38. Wigle, D.A. et al. (2002) Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res., 62, 3005-3008.
39. Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B, 68, 49-67.
40. Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B, 67, 301-301.