Abstract
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.
This work was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61163036, No. 61163039. The Natural Science Foundation of Gansu under grant No.1010RJZA022 and No.1107RJZA112. The Fundamental Research Funds for Universities of Gansu Province in 2012. The Foundation of Gansu University Graduate Tutor No.1201-16. The third Knowledge Innovation Project of Northwest Normal University No.nwnu-kjcxgc-03-67.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Quackenbush, J.: Microarray Analysis and Tumor Classification. The New England Journal of Medicine 354(23), 2463–2472 (2006)
Levi, D., Ullman, S.: Learning to Classify by Ongoing Feature Selection. Image and Vision Computing 28(4), 715–723 (2010)
Lee, Z.-J.: An Integrated Algorithm for Gene Selection and Classification Applied to Microarray Data of Ovarian Cancer. Artificial Intelligence in Medicine 42, 81–93 (2008)
Moon, H., Ahn, H., Kodell, R.L., Back, S., Lin, C.-J.: Ensemble Methods for Classification of Patients for Personalized Medicine with High-dimensional Data. Artificial Intelligence in Medicine 41, 197–207 (2007)
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)
Yeh, J.-Y.: Applying Data Mining Techniques for Cancer Classification on Gene Expression Data. Cybernetics and Systems: An International Journal 39, 583–602 (2008)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Tan, A.C., Gilbert, D.: Ensemble Machine Learning on Gene Expression Data for Cancer Classification. Applied Bioinformatics 2(3), S75–S83 (2003)
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)
Li, H., Wang, J.-L.: Study of Tumor Molecular Prediction Model based Gene Expression Profiles. Acta Electronica Sinica 36(5), 989–992 (2008)
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)
Hu, Q., Yu, D., Xie, Z.: Neighborhood Classifiers. Expert Systems with Applications 34, 866–876 (2008)
Specht, D.F.: Probabilistic Neural Networks. Neural Networks 3, 109–118 (1990)
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)
Boussioutas, A., Li, H., Liu, J., et al.: Distinctive Patterns of Gene Expression in Premalignant Gastric Cancer. Cancer Research 63, 2569–2577 (2003)
Petricoin, E., Ardekani, A., Hitt, B., et al.: Use of Proteomic Patterns in Serum to Identify Ovarian Cancer. Lancet 359, 572–577 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yun, J., Guocheng, X., Na, C., Shan, C. (2013). A New Gene Expression Profiles Classifying Approach Based on Neighborhood Rough Set and Probabilistic Neural Networks Ensemble. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-42042-9_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
eBook Packages: Computer ScienceComputer Science (R0)