Tissue Classification Using Gene Expression Data and Artificial Neural Network Ensembles

  • Huijuan Lu
  • Jinxiang Zhang
  • Lei Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


An important challenge in the use of large-scale gene expression data for biological classification occurs when the number of genes far exceeds the number of samples. This situation will make the classification results are unstable. Thus, a tissue classification method using artificial neural network ensembles was proposed. In this method, a feature preselection method is presented to identify significant genes highly correlated with tissue types. Then pseudo data sets for training the component neural network of ensembles were generated by bagging. The predictions of those individual networks were combined by simple averaging method. Some data experiments have shown that this classification method yields competitive results on several publicly available datasets.


Artificial Neural Network Gene Expression Data Receiver Operator Characteristic Curve Neural Network Ensemble Tissue Classification 
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.
    Anand, R., Mehrotra, K., Mohan, C.K., Ranka, S.: Efficient Classification for Multiclass Problems Using Modular Neural Networks. IEEE Transactions on Neural Networks 6, 117–124 (1995)CrossRefGoogle Scholar
  2. 2.
    Alon, U., Barkai, N., Notterman, D., 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. Proceedings of the National Academy of Sciences 96, 6745–6750 (1999)CrossRefGoogle Scholar
  3. 3.
    Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, Z.: Tissue Classification with Gene Expression Profiles. Journal of Computational Biology 7, 559–583 (2000)CrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)MATHMathSciNetGoogle Scholar
  5. 5.
    Breiman, L.: Using Convex Pseudo-Data to Increase Prediction Accuracy. Technical Report 513, Statistics Department, U.C. Berkeley, USA (1998)Google Scholar
  6. 6.
    DeRisi, J.L., Iyer, V.R., Brown, P.O.: Exploring The Metabolic and Genetic Control of Gene Expression on A Genomic Scale. Science 278, 680–686 (1997)CrossRefGoogle Scholar
  7. 7.
    Dudoit, S., Fridlyand, J., Speed, T.: Comparison of Discrimination Methods for the Classification of Tumors using Gene Expression Data. Journal of the American Statistical Association 97, 77–87 (2002)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Fort, G., Lambert-Lacroix, S.: Classification Using Partial Least Squares with Penalized Logistic Regression. Bioinformatics 21, 1104–1111 (2005)CrossRefGoogle Scholar
  9. 9.
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization Of On-Line Learning And An Application To Boosting. Journal of Computer and System Sciences 55, 119–139 (1997)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, M., 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, 531–537 (1999)CrossRefGoogle Scholar
  11. 11.
    Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)CrossRefGoogle Scholar
  12. 12.
    Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., Meltzer, P.S.: Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks. Nature Medicine 7, 673–679 (2001)CrossRefGoogle Scholar
  13. 13.
    Khan, J., Simon, R., Bittner, M., Chen, Y., Leighton, S.B., Pohida, T., Smith, P.D., Jiang, Y., Gooden, G.C., Trent, J.M., Meltzer, P.S.: Gene Expression Profiling of Alveolar Rhabdomyosarcoma with cDNA Microarrays. Cancer Research 58, 5009–5013 (1998)Google Scholar
  14. 14.
    Lockhart, D.J., Dong, H., Byrne, M.C., Follettie, M.T., Gallo, M.V., Chee, M.S., Mittmann, M., Wang, C., Kobayashi, M., Horton, H., Brown, E.L.: Expression Monitoring by Hybridization to High-Density Oligonucleotide Arrays. Nature Biotechnology 14, 1675–1680 (1996)CrossRefGoogle Scholar
  15. 15.
    O’Neill, M.C., Song, L.: Neural Network Analysis of Lymphoma Microarray Data: Prognosis Diagnosis Near-perfect. BMC Bioinformatics 4, 13 (2003)CrossRefGoogle Scholar
  16. 16.
    Park, P., Pagano, M., Bonetti, M.A.: Nonparametric Scoring Algorithm for Identifying Informative Genes from Microarray Data. In: Pacific Symposium on Biocomputing, vol. 6, pp. 52–63 (2001)Google Scholar
  17. 17.
    Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J.P., Poggio, T., Gerald, W., Loda, M., Lander, E.S., Golub, T.R.: Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures. Proceedings of the National Academy of Sciences 98, 15149–15154 (2001)CrossRefGoogle Scholar
  18. 18.
    Schapire, R.E.: The Strength Of Weak Learnability. Machine Learning 5, 197–227 (1990)Google Scholar
  19. 19.
    West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson Jr., J.A., Marks, J.R., Nevins, J.R.: Predicting the Clinical Status of Human Breast Cancer by Using Gene Expression Profiles. Proceedings of the National Academy of Sciences 98, 11462–11467 (2001)CrossRefGoogle Scholar
  20. 20.
    Xu, Y., Selaru, F.M., Yin, J., Zou, T.T., Shustova, V., Mori, Y., Sato, F., Liu, T.C., Olaru, A., Wang, S., Kimos, M.C., Perry, K., Desai, K., Greenwald, B.D., Krasna, M.J., Shibata, D., Abraham, J.M., Meltzer, S.J.: Artificial Neural Networks and Gene Filtering Distinguish between Global Gene Expression Profiles of Barrett’s Esophagus and Esophageal Cancer. Cancer Research 62, 3493–3497 (2002)Google Scholar
  21. 21.
    Yeung, K.Y., Bumgarner, R.E., Raftery, A.E.: Bayesian Model Averaging: Development of an Improved Multi-Class, Gene Selection and Classification Tool for Microarray Data. Bioinformatics 21, 2394–2402 (2005)CrossRefGoogle Scholar
  22. 22.
    Zhou, Z.H., Chen, S.F.: Neural Network Ensemble. Chinese Journal of Computers 25, 1–8 (2002)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huijuan Lu
    • 1
    • 2
  • Jinxiang Zhang
    • 3
  • Lei Zhang
    • 1
  1. 1.Institute of Computer ApplicationsChina Jiliang UniversityHangzhouChina
  2. 2.College of Computer ScienceZhejiang UniversityHangzhouChina
  3. 3.Department of Computer ScienceZhejiang Education InstituteHangzhouChina

Personalised recommendations