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Title: Accurate Classification of SAGE Data Based on Frequent Patterns of Gene Expression
Author(s): G. Tzanis, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (5 pages).
Keywords: Classification, Gene Expression, SAGE, Cancer, Frequent Patterns.
Appeared in: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), IEEE, Patras, Greece, 2007.
Abstract: In this paper we present a method for classifying accurately SAGE (Serial Analysis of Gene Expression) data. The high dimensionality of the data, namely the large number of features, in combination with the small number of samples poses a great challenge and demands more accurate and robust algorithms for classification. The prediction accuracy of the up to now proposed approaches is moderate. In our approach we exploit the associations among the expressions of genes in order to construct more accurate classifiers. For validating the effectiveness of our approach we experimented with two real datasets using numerous feature selection and classification algorithms. The results have shown that our approach improves significantly the classification accuracy, which reaches 99%.
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        This paper has been cited by the following:

1 Yang, C.-H., Shih, T.-M., and Chuang, L.-Y. “Reducing SAGE Data Using Genetic Algorithms”, In Proceedings of World Academy of Science, Engineering and Technology vol. 41 May 2009, ISSN: 2070-374.