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- Title
Simple decision rules for classifying human cancers from gene expression profiles.
- Authors
Tan, Aik Choon; Naiman, Daniel Q; Xu, Lei; Winslow, Raimond L; Geman, Donald
- Abstract
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.
- Publication
Bioinformatics (Oxford, England), 2005, Vol 21, Issue 20, p3896
- ISSN
1367-4803
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/bti631