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.
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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
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DOI: https://doi.org/10.1007/978-3-642-42042-9_60
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