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

  • Statnikov A
  • Aliferis C
  • Tsamardinos I
 et al. 
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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

Author-supplied keywords

  • $Microarray
  • $SVM
  • Algorithms
  • Classification
  • Gene Expression
  • IS
  • Software
  • algorithm
  • analysis
  • cancer
  • diagnosis
  • domain
  • expression
  • gene
  • gene-expression
  • methods
  • model
  • neural networks
  • performance
  • selection
  • system

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  • PMID: 2418


  • A Statnikov

  • C F Aliferis

  • Ioannis Tsamardinos

  • Douglas Hardin

  • Shawn Levy

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