An Application of the Clustering Threshold Gradient Descent Regularization Method for Selecting Genes in Predicting the Survival Time of Lung Carcinomas

  • Published : 2007.09.30

Abstract

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n<

Keywords

References

  1. Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R, Gillette, M., Loda, M., Weber, G., Mark, E.J., Lander, E.S., Wong, W., Johnson, B.E., GolUb, T.R, Sugarbaker, D.J., and Meyerson, M. (2001). Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA 98, 13790-13795
  2. Cox, D.R (1972). Regression models and life tables (with discussion). J.R. Stat. Soc. B. 34, 187-202
  3. Friedman, J.H. and Popescue, B.E. (2004). Gradient directed regularization for linear regression and classification. Technical report, Department of Statistics, Stanford University. http://www-stat.stanford.edul-jhf/PathSeeker. html
  4. Gui, J. and Li, H. (2005). Threshold gradient descent method for censored data regression with applications in pharmacogenomics. Proc. Pac. Symp. Biocomput. 10,272-283
  5. Hastie, T and Tibshirani, R (1990). Generalized Additive Models. Chapman and Hall
  6. Ma, S. and Huang, J. (2007). Clustering threshold gradient descent regularization: with applications to microarray studies. Bioinformatics 23,466-472 https://doi.org/10.1093/bioinformatics/btl632
  7. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., and Golub, T.R. (1999). Interpreting patterns of gene expression with selforganizing maps: Methods and application to hermatopoetic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907-2912
  8. Tibshirani, R (1996). Regression shrinkage and selection via the lasso. J.R. Stat. Soc., B. 58, 267-288
  9. Tibshirani, R. (1997). The lasso method for variable selection in the Cox model. Stat. in Med. 16, 385-395 https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3