Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking

  • Ubaudi A. FrancoEmail author
  • J. Kennedy Paul
  • R. Catchpoole Daniel
  • Guo Dachuan
  • J. Simoff Simeon

Gene expression datasets used in biomedical data mining frequently have two characteristics: they have many thousand attributes but only relatively few sample points and the measurements are noisy. In other words, individual expression measurements may be untrustworthy. Gene Feature Ranking (GFR) is a feature selection methodology that addresses these domain specific characteristics by selecting features (i.e. genes) based on two criteria: (i) how well the gene can discriminate between classes of patient and (ii) the trustworthiness of the microarray data associated with the gene. An example from the pediatric cancer domain demonstrates the use of GFR and compares its performance with a feature selection method that does not explicitly address the trustworthiness of the underlying data.


Feature Selection Acute Lymphoblastic Leukemia Microarray Data Feature Subset Feature Selection Method 
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.
    Hardiman, G.: Microarray technologies - an overview. Pharamacogenomics 3 (2002) 293–297CrossRefGoogle Scholar
  2. 2.
    Schena, M.: Microarray Biochip Technology. BioTechniques Press, Westborough, MA (2000)Google Scholar
  3. 3.
    Bolstad, B., et al.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19 (2003) 185–193CrossRefGoogle Scholar
  4. 4.
    Weng, L., Dai, H., Zhan, Y., He, Y., Stepaniants, S.B., Bassett, D.E.: Rosetta error model for gene expression analysis. Bioinformatics 22 (2006) 1111–1121CrossRefGoogle Scholar
  5. 5.
    Seo, J., Gordish-Dressman, H., Hoffman, E.P.: An interactive power analysis tool for microar-ray hypothesis testing and generation. Bioinformatics 22 (2006) 808–814CrossRefGoogle Scholar
  6. 6.
    Tsai, C.A., et al.: Sample size for gene expression microarray experiments. Bioinformatics 21 (2005) 1502–1508CrossRefGoogle Scholar
  7. 7.
    Baldi, P., Hatfield, G.W.: DNA Microarrays and Gene Expression: from experiments to data analysis and modeling. Cambridge University Press (2002)Google Scholar
  8. 8.
    Golub, T., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286 (1999) 7CrossRefGoogle Scholar
  9. 9.
    Mukherjee, S., Tamayo, P., Slonim, D.K., Verri, A., Golub, T.R., Mesirov, J.P., Poggio, T.: Support vector machine classification of microarray data. AI memo 182. CBCL paper 182. Technical report, MIT (2000) Can be retrieved from Scholar
  10. 10.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97 (1997) 245–271zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Yang, J., Hanavar, V.: Feature subset selection using a genetic algorithm. Technical report, Iowa State University (1997+)Google Scholar
  12. 12.
    Efron, B., Tibshirani, R., Goss, V., Chu, G.: Microarrays and their use in a comparative experiment. Technical report, Stanford University (2000)Google Scholar
  13. 13.
    Bellman, R.E.: Adaptive Control Processes. Princeton University Press (1961)zbMATHGoogle Scholar
  14. 14.
    John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Eleventh International Conference (Machine Learning), Kaufmann Morgan (1994) 121–129Google Scholar
  15. 15.
    Saeys, Y., Inza, I., et al.: A review of feature selection tecnhiques in bioinformatics. Bioinfor-matics 23 (2007) 2507–2517CrossRefGoogle Scholar
  16. 16.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Machine Learning Research (2003) 1157–1182Google Scholar
  17. 17.
    Wang, X., Ghosh, S., Guo, S.W.: Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Research 29 (2001) 8CrossRefGoogle Scholar
  18. 18.
    Park, T., Yi, S.G., Lee, S., Lee, J.K.: Diagnostic plots for detecting outlying slides in a cDNA microarray experiment. BioTechniques 38 (2005) 463–471CrossRefGoogle Scholar
  19. 19.
    Yu, Y., Khan, J., et al.: Expression profiling identifies the cytoskeletal organizer ezrin and the developmental homeoprotein six- 1 as key metastatic regulators. Nature Medicine 10 (2004) 175–181CrossRefGoogle Scholar
  20. 20.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1 (1986) 81–106Google Scholar
  21. 21.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  22. 22.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, San Francisco, Morgan Kaufmann (1996) 148–156Google Scholar
  23. 23.
    Dawson, B., Trapp, R.G.: Basic & Clinical Biostatistics. Third edn. Health Professions. McGraw-Hill Higher Education, Singapore (2001)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Ubaudi A. Franco
    • 1
    Email author
  • J. Kennedy Paul
    • 1
  • R. Catchpoole Daniel
    • 1
  • Guo Dachuan
    • 1
  • J. Simoff Simeon
    • 1
  1. 1.Faculty of IT, University of TechnologySydney

Personalised recommendations