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 language||English (US)|
|Number of pages||6|
|Journal||Computational Biology and Chemistry|
|State||Published - Aug 2012|
Bibliographical noteFunding 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.
- Microarray data