A Gene Selection Algorithm using Bayesian Classification Approach

In this study, we propose a new feature (or gene) s election algorithm using Bayes classification approach. The algorithm can find gen e subset crucial for cancer classification problem. Problem statement: Gene identification plays important role in human c ancer classification problem. Several feature selection algorithms have been prop osed 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 classifi cation 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 a pproach. Results: This approach gives promising results on gene expression datasets and compares fa vorably 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 h igh classification accuracy on DNA microarray gene expression datasets.

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