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Classification and selection of biomarkers in genomic data using LASSO

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  • Debashis Ghosh

    (University of Michigan)

  • Arul Chinnaiyan

    (University of Michigan Pathology and Urology)

Abstract

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression 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 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.

Suggested Citation

  • Debashis Ghosh & Arul Chinnaiyan, 2004. "Classification and selection of biomarkers in genomic data using LASSO," The University of Michigan Department of Biostatistics Working Paper Series 1041, Berkeley Electronic Press.
    • Handle: RePEc:bep:mchbio:1041
    Note: oai:bepress.com:umichbiostat-1041
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    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1041&context=umichbiostat
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    References listed on IDEAS

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    1. Saravana M. Dhanasekaran & Terrence R. Barrette & Debashis Ghosh & Rajal Shah & Sooryanarayana Varambally & Kotoku Kurachi & Kenneth J. Pienta & Mark A. Rubin & Arul M. Chinnaiyan, 2001. "Delineation of prognostic biomarkers in prostate cancer," Nature, Nature, vol. 412(6849), pages 822-826, August.
    2. Stuart G. Baker, 2000. "Identifying Combinations of Cancer Markers for Further Study as Triggers of Early Intervention," Biometrics, The International Biometric Society, vol. 56(4), pages 1082-1087, December.
    3. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
    4. Martin W. McIntosh & Margaret Sullivan Pepe, 2002. "Combining Several Screening Tests: Optimality of the Risk Score," Biometrics, The International Biometric Society, vol. 58(3), pages 657-664, September.
    Full references (including those not matched with items on IDEAS)

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