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Stud Health Technol Inform. 2004;107(Pt 2):813-7.

Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development.

Author information

1
Discovery Systems Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA. alexander.statnikov@vanderbilt.edu

Abstract

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. In order to equip the system with the optimal combination of data modeling methods, 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.

PMID:
15360925
[Indexed for MEDLINE]

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