A Novel Method of Searching the Microarray Data for the Best Gene Subsets by Using a Genetic Algorithm

  • Bin Ni
  • Juan Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)


Searching for a small subset of genes out of the thousands of genes in Microarray is a crucial problem for accurate cancer classification. In this paper, a novel gene selection method based on genetic algorithms (GAs) is proposed. In order to reduce the search space of GAs, a novel pre-selection procedure is also introduced. To evaluate the performance of the presented method, experiments on five open datasets are conducted, and the results show that it performs rather well.


Linear Kernel Gene Subset Gene Selection Method Quadratic Kernel Open Dataset 
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.


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  1. 1.
    Golub, T.R., Slonim, D.K., Tamayo, P., et al.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  2. 2.
    Ben-Dor, A., Bruhn, L., Friedman, N., et al.: Tissue Classification with Gene Expression Profiles. Computational Biology 7, 559–584 (2000)CrossRefGoogle Scholar
  3. 3.
    Alon, U., Barkai, N., Notterman, D., et al.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor Colon Tissues Probed by Oligonucleotide Arrays. PNAS 96, 6745–6750 (1999)CrossRefGoogle Scholar
  4. 4.
    Shipp, M.A., Ross, K.N., Tamayo, P., et al.: Diffuse Large B-cell Lymphoma Outcome Prediction by Gene Expression Profiling and Supervised Machine Learning. Nature Machine 8, 68–74 (2002)CrossRefGoogle Scholar
  5. 5.
    Keller, A.D., Schummer, M., Hood, L., et al.: Bayesian Classification of DNA Array Expression Data, Technical Report UW-CSE-2000-08-01, Department of Computer Science & Engineering, University of Washington, Seattle (2000)Google Scholar
  6. 6.
    Ding, C., Peng, H.: Minimum Redundancy Feature Selection from Microarray Gene Expression Data. In: CSB 2003, pp. 523–529 (2003)Google Scholar
  7. 7.
    Chen, X.-w.: Gene Selection for Cancer Classification Using Bootstrapped Genetic Algorithms and Support Vector Machines. In: CSB 2003, pp. 504–505 (2003)Google Scholar
  8. 8.
    Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., et al.: Prediction of Central Nervous System Embryonal Tumor Outcome Based on Gene Expression. Nature 415, 436–442 (2002)CrossRefGoogle Scholar
  9. 9.
    Yang, Y.H., Dudoit, S., Lin, D.M., et al.: Normalization for cDNA Microarray Data: a Robust Composite Method Addressing Single and Multiple Slide Systematic Variation. Nucleic Acids Res 10, e15.1-e15.10 (2002)Google Scholar
  10. 10.
    Krishnapuram, B., Carin, L., Hartemink, A.: Joint Classifier and Feature Optimizationi for Cancer Diagnosis Using Gene Expression Data. Journal of Computational Biology (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Bin Ni
    • 1
  • Juan Liu
    • 1
    • 2
  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.The State Key Lab. of Soft. EngWuhan UniversityWuhanChina

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