Advertisement

Scoring Method for Tumor Prediction from Microarray Data Using an Evolutionary Fuzzy Classifier

  • Shinn-Ying Ho
  • Chih-Hung Hsieh
  • Kuan-Wei Chen
  • Hui-Ling Huang
  • Hung-Ming Chen
  • Shinn-Jang Ho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

In this paper, we propose a novel scoring method for tumor prediction using an evolutionary fuzzy classifier which can provide accurate and interpretable information. The merits of the proposed method are threefold. 1) The score ranged in [0, 100] can further illustrate the degree of tumor status in contrast to the conventional tumor classifier. 2) The derived score system can be used as a tumor classifier using a system-suggested or human-specified threshold value. 3) The derived classifier with a compact fuzzy rule base can generate an interpretable and accurate prediction result. The effectiveness of the proposed method is evaluated and compared using two well-known datasets from microarray data and an existing tumor classifier. It is shown by computer simulation that the proposed scoring method is effective using ROC curves of classification.

Keywords

Support Vector Machine Fuzzy Rule Probabilistic Neural Network Tumor Classifier Fuzzy Classifier 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ressom, H., Reynolds, R., Varghese, R.S.: Increasing the efficiency of fuzzy logic-based gene expression data analysis. Physiol Genomics 13, 107–117 (2003)CrossRefGoogle Scholar
  2. 2.
    Woolf, P.J., Wang, Y.A.: Fuzzy logic approach to analyzing gene expression data. Physiol Genomics 3, 9–15 (2000)Google Scholar
  3. 3.
    Kauffman, S., Peterson, C., Samuelsson, B., Troein, C.: Random boolean network models and the yeast transcriptional network. PNAS 100(25), 14796–14799 (2003)CrossRefGoogle Scholar
  4. 4.
    Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19(1), 79–86 (2003)CrossRefGoogle Scholar
  5. 5.
    Soinov, L.A., Krestyaninova, M.A., Brazma, A.: Towards reconstruction of gene networks from expression data by supervised learning. Genome Biology 4(R6) (2003)Google Scholar
  6. 6.
    Li, J., Liu, H., Downing, J.R., Yeoh, A.E.-J., Wong, L.: Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (all) patients. Bioinformatics 19(1), 71–78 (2003)CrossRefGoogle Scholar
  7. 7.
    Hvidsten, T.R., Lgreid, A., Komorowski, J.: Learning rulebased models of biological process from gene expression time profiles using gene ontology. Bioinformatics 19(9), 1116–1123 (2003)CrossRefGoogle Scholar
  8. 8.
    Vinterbo, S.A., Kim, E.-Y., Ohno-Machado, L.: Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 21, 1964–1970 (2005)CrossRefGoogle Scholar
  9. 9.
    Friberg, M., Rohr, P., Gonnet, G.: Scoring functions for transcription factor binding site prediction. BMC Bioinformactics 6(84) (2005)Google Scholar
  10. 10.
    Liu, X.S., Brutlag, D.L., Liu, J.S.: An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat Biotechnol 20, 835–839 (2002)CrossRefGoogle Scholar
  11. 11.
    Murvai, J., Vlahovicek, K., Pongor, S.: A simple probabilistic scoring method for protein domain identification. Bioinformatics 16(12), 1155–1156 (2000)CrossRefGoogle Scholar
  12. 12.
    Jensen, S.T., Liu, J.S.: BioOptimizer: a Bayesian scoring function approach to motif discovery. Bioinformatics 20(10), 1557–1564 (2004)CrossRefGoogle Scholar
  13. 13.
    Ho, S.-Y., Chen, H.-M., Ho, S.-J., Chen, T.-K.: Design of Accurate Classifiers with a Compact Fuzzy-Rule Base Using an Evolutionary Scatter Partition of Feature Space. IEEE Trans. Systems, Man, and Cybernetics-Part B 34(2), 1031–1044 (2004)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ho, S.-Y., Shu, L.-S., Chen, J.-H.: Intelligent Evolutionary Algorithms for Large Parameter Optimization Problems. IEEE Trans. Evolutionary Computation 8(6), 522–541 (2004)CrossRefGoogle Scholar
  15. 15.
    Statnikov, A., Aliferis, C.F., Tsamardinos, I., Hardin, D., Levy, S.: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21, 631–643 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shinn-Ying Ho
    • 1
  • Chih-Hung Hsieh
    • 1
  • Kuan-Wei Chen
    • 1
  • Hui-Ling Huang
    • 2
  • Hung-Ming Chen
    • 3
  • Shinn-Jang Ho
    • 4
  1. 1.Institute of BioinformaticsNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Department of Information ManagementJin Wen Institute of TechnologyHsin-Tien, TaipeiTaiwan
  3. 3.Institute of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan
  4. 4.Department of Automation EngineeringNational Formosa UniversityHuwei, YunlinTaiwan

Personalised recommendations