Sparse regularized discriminant analysis with application to microarrays |
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| Affiliation: | 1. School of Chemistry, College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Islamic Republic of Iran;2. Department of Chemistry, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan;1. Department of Organic Chemistry, Faculty of Pharmacy, Wrocław Medical University, Borowska Street 211A, 50-556 Wrocław, Poland;2. Computer Modeling Centre, DMW Communication, Świętokrzyska Street 40b, 50-327 Wrocław, Poland |
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| 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 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. |
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