| 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%. |
| See also : |
|
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. |
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