title: Sparse regularized discriminant analysis with application to microarrays.
abstract: 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 ℓ(1) 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.
authors: Division of Biostatistics, School of Public Health University of Minnesota, Minneapolis, MN 55455, USA.
authors: Ran
authors: R
authors: Li
authors: Baolin
authors: B
authors: Wu
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D003198:Computer Simulation; D016002:Discriminant Analysis; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000235:genetics*