Gene Expression Profiling Using Flexible Neural Trees

  • Yuehui Chen
  • Lizhi Peng
  • Ajith Abraham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


This paper proposes a Flexible Neural Tree (FNT) model for informative gene selection and gene expression profiles classification. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Extended Compact Genetic Programming and the free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results on two well-known cancer datasets shows competitive results with existing methods.


Particle Swarm Optimization Random Forest Informative Gene Global Good Position Leukemia 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. Topon, K.P., Hitoshi, I.: Gene Selection for Classification of Cancers using Probabilistic Model Building Genetic Algorithm. BioSystems 82(3), 208–225 (2005)CrossRefGoogle Scholar
  2. Hong, J.-H., Cho, S.-B.: The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming. Artificial Intelligence in Medicine 36, 43–58 (2006)CrossRefGoogle Scholar
  3. Asyali, M.H., Colak, D., Demirkaya, O., Inan, M.S.: Gene Expression Profile Classification: A Review. Current Bioinformatics 1, 55–73 (2006)CrossRefGoogle Scholar
  4. Daz-Uriarte, R., de Andrs, S.A.: Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3 (2006)CrossRefGoogle Scholar
  5. Chen, Y., Yang, B., Dong, J.: Nonlinear System Modeling via Optimal Design of Neural Trees. International Journal of Neural Systems 14, 125–137 (2004)CrossRefGoogle Scholar
  6. Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series Forecasting using Flexible Neural Tree Model. Information Science 174, 219–235 (2005)CrossRefMathSciNetGoogle Scholar
  7. Sastry, K., Goldberg, D.E.: Probabilistic model building and competent genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practise, pp. 205–220 (2003)Google Scholar
  8. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, J.P., Mesirov, J., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  9. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. on Neural Networks, Perth, pp. 1492–1948 (1995)Google Scholar
  10. Frohlich, H., Chapelle, O., Scholkopf, B.: Feature Selection for Support Vector Ma-chines by Means of Genetic Algorithms. In: 15th IEEE International Conference on Tools with Artificial Intelligence, p. 142 (2003)Google Scholar
  11. Xue-wen, C.: Gene Selection for Cancer Classification Using Bootstrapped Genetic Algorithms and Support Vector Machines. IEEE Computer Society Bioinformatics Conference, p. 504 (2003)Google Scholar
  12. Nguyen, H.-N., Ohn, S.-Y., Park, J., Park, K.-S.: Combined Kernel Function Approach in SVM for Diagnosis of Cancer. In: Proceedings of the First International Conference on Natural Computation (2005)Google Scholar
  13. Su, T., Basu, M., Toure, A.: Multi-Domain Gating Network for Classification of Cancer Cells using Gene Expression Data. In: Proceedings of the International Joint Conference on Neural Networks, pp. 286–289 (2002)Google Scholar
  14. Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays. Proceedings of National Academy of Sciences of the United States of American 96, 6745–6750 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Lizhi Peng
    • 1
  • Ajith Abraham
    • 1
    • 2
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R. China
  2. 2.IITA Professorship Program, School of Computer Science and Engg.Chung-Ang UniversitySeoulRepublic of Korea

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