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Bioinformatics Advance Access originally published online on August 16, 2005
Bioinformatics 2005 21(20):3896-3904; 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{at}oxfordjournals.org

Simple decision rules for classifying human cancers from gene expression profiles

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

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

*To whom correspondence should be addressed.

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 that 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 datasets involving human cancers. The k-TSP classifier performs as efficiently as Prediction Analysis of Microarray and support vector machine, and outperforms other learning methods (decision trees, k-nearest neighbour 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 datasets are available at http://www.ccbm.jhu.edu

Contact: actan{at}jhu.edu

Received on May 9, 2005; revised on July 28, 2005; accepted on August 14, 2005

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