Personalized Modeling Based Gene Selection for Microarray Data Analysis

  • Yingjie Hu
  • Qun Song
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)


This paper presents a novel gene selection method based on personalized modeling. Identifying a compact set of genes from gene expression data is a critical step in bioinformatics research. Personalized modeling is a recently introduced technique for constructing clinical decision support systems. In this paper we have provided a comparative study using the proposed Personalized Modeling based Gene Selection method (PMGS) on two benchmark microarray datasets (Colon cancer and Central Nervous System cancer data). The experimental results show that our method is able to identify a small number of informative genes which can lead to reproducible and acceptable predictive performance without expensive computational cost. These genes are of importance for specific groups of people for cancer diagnosis and prognosis.


Gene Selection Clinical Decision Support System Microarray Data Analysis Informative Gene Wrap 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.
    Nevins, J.R., Huang, E.S., Dressman, H., Pittman, J., Huang, A.T., West, M.: Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Human Molecular Genetics 12(2), R153–R157 (2003)Google Scholar
  2. 2.
    Song, Q., Kasabov, N.: Twnfi - a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling. Neural Networks 19(10), 1591–1596 (2006)CrossRefzbMATHGoogle Scholar
  3. 3.
    Hu, Y., Kasabov, N.: ntology-based framework for personalized diagnosis and prognosis of cancer based on gene expression data. In: ICONIP 2007 14th International Conference on Neural Information Processing, Kitakyushu City, Fukuoka, Japan, vol. 2, pp. 846–855 (2007)Google Scholar
  4. 4.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  5. 5.
    Kasabov, N.: Global, local and personalized modelling and pattern discovery in bioinformatics: An integrated approach. Pattern Recognition Letters 28, 673–685 (2007)CrossRefGoogle Scholar
  6. 6.
    Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evolutionary Computation 3(4), 287–297 (1999)CrossRefGoogle Scholar
  7. 7.
    Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci., USA 96, 6745–6750 (1999)Google Scholar
  8. 8.
    Pomeroy, S., Tamayo, P., et al.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(6870), 422–436 (2002)CrossRefGoogle Scholar
  9. 9.
    Ransohoff, D.F.: Bias as a threat to the validity of cancer molecular-marker research. Nat. Rev. Cancer 5(2), 142–149 (2005)CrossRefGoogle Scholar
  10. 10.
    Ioannidis, J.P.A.: Microarrays and molecular research: noise discovery? Lancet 365, 453–455 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yingjie Hu
    • 1
  • Qun Song
    • 1
  • Nikola Kasabov
    • 1
  1. 1.The Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand

Personalised recommendations