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

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Diagnosis, Computer-Assisted
  • Expert Systems
  • Factor Analysis, Statistical
  • Gene Expression
  • Gene Expression Profiling / classification*
  • Humans
  • Neoplasms / diagnosis*
  • Neoplasms / genetics*
  • Oligonucleotide Array Sequence Analysis*
  • Pattern Recognition, Automated
  • Software