Sharma, Alokanand and Paliwal, K.K. (2012) A gene selection algorithm using Bayesian classification approach. American Journal of Applied Sciences, 9 (1). pp. 127-131. ISSN 1546-9239
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Abstract
In this study, we propose a new feature (or gene) selection algorithm using Bayes classification approach. The algorithm can find gene subset crucial for cancer classification problem.
Problem statement: Gene identification plays important role in human cancer classification problem.
Several feature selection algorithms have been proposed for analyzing and understanding influential genes using gene expression profiles.
Approach: The feature selection algorithms aim to explore genes that are crucial for accurate cancer classification and also endure biological significance. However, the performance of the algorithms is still limited. In this study, we propose a feature selection algorithm using Bayesian classification approach.
Results: This approach gives promising results on gene expression datasets and compares favorably with respect to several other existing techniques.
Conclusion: The proposed gene selection algorithm using Bayes classification approach is shown to find important genes that can provide high classification accuracy on DNA microarray gene expression datasets.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Ms Shalni Sanjana |
| Date Deposited: | 25 Jun 2011 04:08 |
| Last Modified: | 18 Jan 2017 03:28 |
| URI: | https://repository.usp.ac.fj/id/eprint/4816 |
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