Abstract
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.
This work was supported by the Zhejiang Provincial Major Scientific and Technological Project (No.2005C21028) and Zhejiang Provincial Natural Science Foundation of China (No.Y109456 ).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Breiman, L.: Using Convex Pseudo-Data to Increase Prediction Accuracy. Technical Report 513, Statistics Department, U.C. Berkeley, USA (1998)
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)
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)
Fort, G., Lambert-Lacroix, S.: Classification Using Partial Least Squares with Penalized Logistic Regression. Bioinformatics 21, 1104–1111 (2005)
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)
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)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
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)
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)
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)
O’Neill, M.C., Song, L.: Neural Network Analysis of Lymphoma Microarray Data: Prognosis Diagnosis Near-perfect. BMC Bioinformatics 4, 13 (2003)
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)
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)
Schapire, R.E.: The Strength Of Weak Learnability. Machine Learning 5, 197–227 (1990)
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)
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)
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)
Zhou, Z.H., Chen, S.F.: Neural Network Ensemble. Chinese Journal of Computers 25, 1–8 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, H., Zhang, J., Zhang, L. (2006). Tissue Classification Using Gene Expression Data and Artificial Neural Network Ensembles. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_85
Download citation
DOI: https://doi.org/10.1007/11816102_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
Online ISBN: 978-3-540-37282-0
eBook Packages: Computer ScienceComputer Science (R0)