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Sparse regularized discriminant analysis with application to microarrays

Paper ID Volume ID Publish Year Pages File Format Full-Text
15198 1390 2012 6 PDF Available
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 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.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► A unified modeling approach that simultaneously analyzes gene interactions and selects important genes for improved prediction of microarrays. ► Very efficient computational algorithms developed for model estimation and selection. ► Demonstrate the very competitive performance of the proposed method.

Keywords
Lasso; Microarray data; PCA; Prediction
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 39, August 2012, Pages 14–19
Authors
, ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us