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University of Michigan School of Public Health The University of Michigan Department of Biostatistics Working Paper Series Year Paper Classification and selection of biomarkers in genomic data using LASSO Debashis Ghosh * Arul Chinnaiyan * University of Michigan, [email protected] University of Michigan Pathology and Urology, [email protected] This working paper is hosted by The Berkeley Electronic Press (bepress) and may not be commer- cially reproduced without the permission of the copyright holder. http://biostats.bepress.com/umichbiostat/paper42 Copyright c 2004 by the authors.

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Page 1: University of Michigan School of Public Health · 2017. 2. 12. · Classification and selection of biomarkers in genomic data using LASSO Debashis Ghosh and Arul Chinnaiyan Abstract

University of Michigan School of PublicHealth

The University of Michigan Department of Biostatistics WorkingPaper Series

Year Paper

Classification and selection of biomarkers ingenomic data using LASSO

Debashis Ghosh∗ Arul Chinnaiyan†

∗University of Michigan, [email protected]†University of Michigan Pathology and Urology, [email protected]

This working paper is hosted by The Berkeley Electronic Press (bepress) and may not be commer-cially reproduced without the permission of the copyright holder.

http://biostats.bepress.com/umichbiostat/paper42

Copyright c©2004 by the authors.

Page 2: University of Michigan School of Public Health · 2017. 2. 12. · Classification and selection of biomarkers in genomic data using LASSO Debashis Ghosh and Arul Chinnaiyan Abstract

Classification and selection of biomarkers ingenomic data using LASSO

Debashis Ghosh and Arul Chinnaiyan

Abstract

High-throughput gene expression technologies such as microarrays have been uti-lized in a variety of scientific applications. Most of the work has been on assessingunivariate associations between gene expression with clinical outcome (variableselection) or on developing classification procedures with gene expression data(supervised learning). We consider a hybrid variable selection/classification ap-proach that is based on linear combinations of the gene expression profiles thatmaximize an accuracy measure summarized using the receiver operating char-acteristic curve. Under a specific probability model, this leads to considerationof linear discriminant functions. We incorporate an automated variable selectionapproach using LASSO. An equivalence between LASSO estimation with sup-port vector machines allows for model fitting using standard software. We applythe proposed method to simulated data as well as data from a recently publishedprostate cancer study.

Page 3: University of Michigan School of Public Health · 2017. 2. 12. · Classification and selection of biomarkers in genomic data using LASSO Debashis Ghosh and Arul Chinnaiyan Abstract

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