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Bioinformatics. 2005 Mar 1;21(5):631-43. Epub 2004 Sep 16.

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis.

Author information

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

Abstract

MOTIVATION:

Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types.

RESULTS:

Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets.

AVAILABILITY:

The software system GEMS is available for download from http://www.gems-system.org for non-commercial use.

CONTACT:

alexander.statnikov@vanderbilt.edu.

PMID:
15374862
DOI:
10.1093/bioinformatics/bti033
[Indexed for MEDLINE]
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