Feature Selection and Ranking of Key Genes for Tumor Classification: Using Microarray Gene Expression Data

  • Srinivas Mukkamala
  • Qingzhong Liu
  • Rajeev Veeraghattam
  • Andrew H. Sung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computational intelligent techniques for classification accuracy on Leukemia, Lymphoma and Prostate cancer datasets of broad institute and Colon cancer dataset from Princeton gene expression project. Classification accuracy is evaluated with Linear genetic Programs, Multivariate Regression Splines (MARS), Classification and Regression Tress (CART) and Random Forests. Linear Genetic Programs and Random forests perform the best for detecting malignancy of different tumors. Our results demonstrate the potential of using learning machines in diagnosis of the malignancy of a tumor.

We also address the related issue of ranking the importance of input features, which is itself a problem of great interest. Elimination of the insignificant inputs (genes) leads to a simplified problem and possibly faster and more accurate classification of microarray gene expression data. Experiments on select cancer datasets have been carried out to assess the effectiveness of this criterion. Results show that using significant features gives the most remarkable performance and performs consistently well over microarray gene expression datasets we used. The classifiers used perform the best using the most significant features expect for Prostate cancer dataset.


Feature Selection Classification Accuracy Random Forest Multivariate Adaptive Regression Spline Tumor Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, P., Botstein, D.: Exploring the New World of the Genome with DNA Microarrays. Nature Genetics Supplement 21, 33–37 (1999)CrossRefGoogle Scholar
  2. 2.
    Quackenbush, J.: Computational Analysis of Microarray Data. Nature Rev. Genteics 2, 418–427 (2001)CrossRefGoogle Scholar
  3. 3.
    Dudoit, S., Fridlyand, J., Speed, T.: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. J. Am. Statistical Assoc. 97, 77–87 (2002)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Peterson, C., Ringner, M.: Analysis Tumor Gene Expression Profiles. Artificial Intelligence in Medicine 28(1), 59–74 (2003)CrossRefGoogle Scholar
  5. 5.
    Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc. Nat’l Acad. Sci. USA 95, 14863–14868 (1998)CrossRefGoogle Scholar
  6. 6.
    Tamyo, P., et al.: Interpreting Patterns of Gene Expression with Self-Organizing Maps: Methods and Application to Hematopoietic Differentiation. Proc. Nat’l Acad. Sci. USA 96, 2907–2912 (1999)CrossRefGoogle Scholar
  7. 7.
    Armitage, P., Berry, G.: Statistical Methods in Medical Research. Blackwell, Malden (1994)Google Scholar
  8. 8.
    Salford Systems. TreeNet, CART, MARS, Random Forests ManualGoogle Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: Data mining, inference, and prediction. Springer, Heidelberg (2001)MATHGoogle Scholar
  10. 10.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth and Brooks/Cole Advanced Books and Software (1986)Google Scholar
  11. 11.
    Breiman, L.: Random Forests. Journal of Machine Learning 45, 5–32 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)MATHGoogle Scholar
  13. 13.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  14. 14.
    AIM Learning Technology,
  15. 15.
    Golub, T., et al.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  16. 16.
    Shipp, M., et al.: Diffuse Large B-Cell Lymphoma Outcome Prediction by Gene Expression Profiling and Supervised Machine Learning. Nature Medicine 8(1), 68–74 (2002)CrossRefGoogle Scholar
  17. 17.
    Singh, D., et al.: Gene Expression Correlates of Clinical Prostate Cancer Behavior. Cancer Cell 1(2), 227–235 (2002)CrossRefGoogle Scholar
  18. 18.
    Alon, U., et al.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays. Proc. Nat’l Acad. Sci. 96, 6745–6750 (1999)CrossRefGoogle Scholar
  19. 19.
  20. 20.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Srinivas Mukkamala
    • 2
  • Qingzhong Liu
    • 1
  • Rajeev Veeraghattam
    • 1
  • Andrew H. Sung
    • 2
  1. 1.Department of Computer ScienceNew Mexico TechSocorroUSA
  2. 2.Institute for Complex Additive Systems and AnalysisNew Mexico TechUSA

Personalised recommendations