A New Gene Expression Profiles Classifying Approach Based on Neighborhood Rough Set and Probabilistic Neural Networks Ensemble
With the advancement of bioinformatics, the research on the gene chips has been paid more attention by the researchers in recent years. Applications of gene expression profiles on cancer diagnosis and classification have gradually become one of the hot topics in the field of bioinformatics. According to the gene expression profiles characteristics of high dimension and small sample set, we propose a classify method for cancer classification, which is based on neighborhood rough set theory and probabilistic neural network ensemble classification algorithm. Firstly, genes are sorted by using Relief algorithm. Then, classification informative genes are selected using the neighborhood rough set theory. At last, we do cancer classification with probabilistic neural networks ensemble classification model. The experimental results show that the proposed method can effectively select cancer genes, and can obtain better classification results.
KeywordsGene Expression Profiles Neighborhood Rough Set Probabilistic Neural Networks Ensemble Classification Algorithm
Unable to display preview. Download preview PDF.
- 5.Zhang, L.-J., Li, Z.-J.: Gene Selection for Cancer Classification in Microarray Data. Chinese Journal of Computer Research and Development 46(5), 794–802 (2009)Google Scholar
- 8.Tan, A.C., Gilbert, D.: Ensemble Machine Learning on Gene Expression Data for Cancer Classification. Applied Bioinformatics 2(3), S75–S83 (2003)Google Scholar
- 9.Ben-Dor, A., Bruhn, L., Friedman, N., et al.: Tissue Classification with Gene Expression Profiles. In: Proceedings of the Fourth Annual International Conference on Computational Molecular Biology, vol. 5, pp. 1–32 (2000)Google Scholar
- 10.Li, H., Wang, J.-L.: Study of Tumor Molecular Prediction Model based Gene Expression Profiles. Acta Electronica Sinica 36(5), 989–992 (2008)Google Scholar
- 11.Kira, K., Rendell, L.A.: The Feature Selection Problem: Traditional Methods and a New Algorithm. In: Swartout, W. (ed.) Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 129–134. AAAI Press, The MIT Press, San Jose, Cambridge (1992)Google Scholar
- 14.Alon, U., Barkai, N., Notterman, D.A., et al.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays. Cell Biology 96, 6745–6750 (1999)Google Scholar
- 15.Boussioutas, A., Li, H., Liu, J., et al.: Distinctive Patterns of Gene Expression in Premalignant Gastric Cancer. Cancer Research 63, 2569–2577 (2003)Google Scholar