Abstract
A comprehensive study of the database used in Alizadeh et al. [7], about the identification of lymphoma cancer subtypes within Diffuse Large B–Cell Lymphoma (DLBCL), is presented in this paper, focused on both the feature selection and classification tasks. Firstly, we tackle with the identification of relevant genes in the prediction of lymphoma cancer types, and lately the discovering of most relevant genes in the Activated B–Like Lymphoma and Germinal Centre B–Like Lymphoma subtypes within DLBCL. Afterwards, decision trees provide knowledge models to predict both types of lymphoma and subtypes within DLBCL. The main conclusion of our work is that the data may be insufficient to exactly predict lymphoma or even extract functionally relevant genes.
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References
Alizadeh, A.A., Eisen, M., Botstain, D., Brown, P.O., Staudt, L.M.: Probing lymphocyte biology by genomic-scale gene expression analysis. Journal of Clinical Immunology (18), 373–379 (1998)
Han, J., Kamber, M.: Data Mining – Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Liu, H., Motoda, H.: Feature Selection for Knowledge discovery and Data Mining. Kluwer Academic Publishers, Dordrecht (1998)
Gowda, K.C., Krishna, G.: Agglomerative clustering using the concept of mutual nearest neighborhood. Pattern Recognition 10, 105–112 (1977)
Gordon, D.: Classification. Chapman & Hall/CRC, Boca Raton (1999)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth International Group, Belmont (1984)
Alizadeh, A.A., et al.: Distinct types of diffuse large b–cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Harris, N.L., Jaffe, E.S., Diebold, J., Flandrin, G., Muller-Hermelink, H.K., Vardiman, J., Lister, T.A., Bloomfield, C.D.: World health organization classification of neoplastic diseases of the hematopoietic and lymphoid tissues: Report of the clinical advisory committee meeting–airlie house, virginia, November 1997. Journal of Clinical Oncology 17, 3835–3849 (1999)
Hall, M.A.: Correlation–based feature selection for machine learning, Ph.d., Department of Computer Science, University of Waikato, New Zealand (1998)
Kira, K., Rendell, L.: A practical approach to feature selection. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 249–256 (1992)
Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: Proceedings of European Conference on Machine Learning, Springer, Heidelberg (1994)
Liu, H., Setiono, R.: Chi2: Feature selection and discretization of numeric attributes. In: Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence (1995)
Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Azuaje, F.: A computational neural approach to support discovery of gene function and classes of cancer. IEEE Transactions on Biomedical Engineering 48(3), 332–339 (2001)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine learning 11, 63–91 (1993)
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Aguilar-Ruiz, J.S., Azuaje, F. (2004). Knowledge Discovery in Lymphoma Cancer from Gene–Expression. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_5
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DOI: https://doi.org/10.1007/978-3-540-28651-6_5
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