BioMed Research International

BioMed Research International / 2005 / Article
Special Issue

Data Mining in Genomics and Proteomics

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Research article | Open Access

Volume 2005 |Article ID 427208 |

Debashis Ghosh, Arul M. Chinnaiyan, "Classification and Selection of Biomarkers in Genomic Data Using LASSO", BioMed Research International, vol. 2005, Article ID 427208, 8 pages, 2005.

Classification and Selection of Biomarkers in Genomic Data Using LASSO

Received03 Jun 2004
Accepted13 Aug 2004


High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been done on assessing univariate associations between gene expression profiles with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to the consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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