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Gene Selection Strategies in Microarray Expression Data: Applications to Case-Control Studies

  • Gustavo A. StolovitzkyEmail author
Part of the Topics in Biomedical Engineering International Book Series book series (ITBE)

Abstract

Over the last decade we have witnessed the rise of the gene expression array assay as a new experimental paradigm to study the cellular state at the whole genome scale. This technology has allowed considerable progress in the identification of markers associated with human disease mechanisms, and in the molecular characterization of diseases such as cancer, by careful characterization of genes involved directly or indirectly in the disease. A typical gene expression experiment provides scientists with an enormous amount of data. Analysis of these data, and interpretation of the ensuing results, have attracted the attention of many researchers, who have developed new ways of interrogating the expression data. In this chapter we will review some of these recent efforts, emphasizing the need to make use of batteries of methods rather than one method in particular, as well as the need to properly validate results with independent data sets. The application of DNA array technology for use in disease diagnostics will be exemplified in the case of chronic lymphocytic leukemia.

Keywords

Support Vector Machine Chronic Lymphocytic Leukemia Singular Value Decomposition Follicular Lymphoma Gene Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Inc. 2006

Authors and Affiliations

  1. 1.IBM Computational Biology CenterYorktown Heights

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