Cancer diagnosis is a major clinical applications area of gene expression microarray technology. We are seeking to develop a system for cancer diagnostic model creation based on microarray data. We performed a comprehensive evaluation of several major classification algorithms, gene selection methods, and cross-validation designs using 11 datasets spanning 74 diagnostic categories (41 cancer types and 12 normal tissue types). The Multi-Category Support Vector Machine techniques by Crammer and Singer, Weston and Watkins, and one-versus-rest were found to be the best methods and they outperform other learning algorithms such as K-Nearest Neighbors and Neural Networks often to a remarkable degree. Gene selection techniques are shown to significantly improve classification performance. These results guided the development of a software system that fully automates cancer diagnostic model construction with quality on par with or, better than previously published results derived by expert human analysts.
|Original language||English (US)|
|Title of host publication||Studies in Health Technology and Informatics|
|Number of pages||5|
|State||Published - 2004|
- Artificial Intelligence
- Oligonucleotide Array Sequence Analysis
- Support Vector Machines
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, U.S. Gov't, P.H.S.