Abstract
This work aims to explore the use of gene expression data in discriminating heterogeneous cancers. We introduce hybrid learning methodology that integrates genetic algorithms (GA) and artificial neural networks (ANN) to find optimal subsets of genes for tissue/cancer classification. This method was tested on two published microarray datasets: (1) NCI60 cancer cell lines and (2) the GCM dataset. Experimental results on classifying both datasets show that our GA/ANN method not only outperformed many reported prediction approaches, but also reduced the number of predictive genes needed in classification analysis.
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
Ross, D.T., Scherf, U., Eisen, M.B., Perou, C.M., Rees, C., Spellman, P., Iyer, V., Jef-frey, S.S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J.C., Lash-kari, D., Shalon, D., Myers, T.G., Weinstein, J.N., Botstein, D., Brown, P.O.: Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 24, 227–235 (2000)
Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J., Poggio, T., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences USA 98, 15149–15154 (2001)
Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429–2437 (2004)
Ooi, C.H., Tan, P.: Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19, 37–44 (2003)
Deutsch, J.M.: Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 19, 45–52 (2003)
Thanyaluk, J.U., Stuart, A.: Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics 6(148), 1–11 (2005)
Liu, J.J., Cutler, G., Li, W., Pan, Z., Peng, S., Hoey, T., Chen, L., Ling, X.B.: Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 21, 2691–2697 (2005)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal American Statistical Association 97, 77–87 (2002)
Nørgaard, M.: Neural Network Based System Identification Toolbox. Tech. Report. 00-E-891, Department of Automation, Technical University of Denmark (2000)
Khan, J., Wei, J.S., Rigner, M., Saal, L.H., Ladani, M., Westermann, F., Berthold, F., Schwab, M., Antonescus, 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)
Lee, Y., Lee, C.K.: Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19, 1132–1139 (2003)
Cho, H.S., Kim, T.S., Wee, J.W., Jeon, S.M., Lee, C.H.: cDNA Microarray Data Based Classification of Cancers using Neural Networks and Genetic Algorithms. Nanotech 1, 28–31 (2003)
Yeang, C.H., Ramaswamy, S., Tamayo, P., Mukherjee, S., Rifkin, R.M., Angelo, M., Reich, M., Lander, E., Mesirov, J., Golub, T.: Molecular classification of multiple tumor types. Bioinformatics 1, 1–7 (2001)
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
Lin, TC., Liu, RS., Chao, YT., Chen, SY. (2006). Multiclass Microarray Data Classification Using GA/ANN Method. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_129
Download citation
DOI: https://doi.org/10.1007/978-3-540-36668-3_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
eBook Packages: Computer ScienceComputer Science (R0)