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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


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Volume 13, Issue 4

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Multiclass cancer classification based on gene expression comparison

Sitan Yang
  • Corresponding author
  • Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Daniel Q. Naiman
  • Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-06-11 | DOI: https://doi.org/10.1515/sagmb-2013-0053

Abstract

As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an m-class problem, the classification rule typically depends on a small number of m-gene sets, which provide transparent decision boundaries and allow for potential biological interpretations. We first test our approach on seven common gene expression datasets and compare it with popular classification methods including support vector machines and random forests. We then consider an extremely large cohort of leukemia cancer patients to further assess its effectiveness. In both experiments, our method yields comparable or even better results to benchmark classifiers. In addition, we demonstrate that our approach can integrate pathway analysis of gene expression to provide accurate and biological meaningful classification.

Keywords: biomarker discovery; gene expression analysis; multiclass cancer classification

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About the article

Corresponding author: Sitan Yang, Department of Applied Mathematics and Statistics, Johns Hopkins University, 211C Whitehead Hall 3400 N., Charles Street, Baltimore, Maryland 21218, USA, e-mail:


Published Online: 2014-06-11

Published in Print: 2014-08-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 13, Issue 4, Pages 477–496, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2013-0053.

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