Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Methodology article

Feature selection for splice site prediction: A new method using EDA-based feature ranking

Yvan Saeys1, Sven Degroeve1, Dirk Aeyels2, Pierre Rouzé3 and Yves Van de Peer1*

Author Affiliations

1 Department of Plant Systems Biology, Ghent University, Flanders Interuniversity Institute for Biotechnology (VIB), Technologiepark 927, B-9052 Ghent, Belgium

2 SYSTeMS Research Group, Ghent University, Technologiepark 9, B-9052 Ghent, Belgium

3 Laboratoire associé de l'INRA (France), Ghent University, Technologiepark 927, B-9052 Ghent, Belgium

For all author emails, please log on.

BMC Bioinformatics 2004, 5:64  doi:10.1186/1471-2105-5-64

Published: 21 May 2004

Abstract

Background

The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data.

Results

In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing.

Conclusion

We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do) this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.