Sparse regularized discriminant analysis with application to microarrays

Ran Li, Baolin Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


For cancer prediction using large-scale gene expression data, it often helps to incorporate gene interactions in the model. However it is not straightforward to simultaneously select important genes while modeling gene interactions. Some heuristic approaches have been proposed in the literature. In this paper, we study a unified modeling approach based on the ℓ1 penalized likelihood estimation that can simultaneously select important genes and model gene interactions. We will illustrate its competitive performance through simulation studies and applications to public microarray data.

Original languageEnglish (US)
Pages (from-to)14-19
Number of pages6
JournalComputational Biology and Chemistry
StatePublished - Aug 2012

Bibliographical note

Funding Information:
This research was supported in part by NIH grant GM083345 and CA134848 . We would like to thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper.


  • Lasso
  • Microarray data
  • PCA
  • Prediction


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