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Bioinformatics Advance Access published online on August 16, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti631
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 9, 2005
Revised July 28, 2005
Accepted August 14, 2005

Article

Simple decision rules for classifying human cancers from gene expression profiles

Aik Choon Tan 1*, Daniel Q. Naiman 2, Lei Xu 1, Raimond L. Winslow 1, and Donald Geman 2

1 Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA
2 Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA

* To whom correspondence should be addressed.
Aik Choon Tan, E-mail: actan{at}jhu.edu


  Abstract

Motivation: Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. One of the challenges in applying these techniques for classifying gene expression data is to extract accurate, readily interpretable rules providing biological insight as to how classification is performed. Current methods generate classifiers which are accurate but difficult to interpret. This is the trade-off between credibility and comprehensibility of the classifiers. Here, we introduce a new classifier in order to address these problems. It is referred to as k-TSP (k-Top Scoring Pairs) and is based on the concept of "relative expression reversals". This method generates simple and accurate decision rules that only involve a small number of gene-to-gene expression comparisons, thereby facilitating follow-up studies.

Results: In this study, we have compared our approach to other machine learning techniques for class prediction in 19 binary and multi-class gene expression data sets involving human cancers. The k-TSP classifier performs as well as PAM and SVM and outperforms other learning methods (decision trees, k-NN and naïve Bayes). Our approach is easy to interpret as the classifier involves only a small number of informative genes. For these reasons, we consider the k-TSP method to be a useful tool for cancer classification from microarray gene expression data.

Availability: The software and data sets are available at http://www.ccbm.jhu.edu.


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