Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data integration techniques for gene function prediction.
Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction / M. Re, G. Valentini (JMLR WORKSHOP AND CONFERENCE PROCEEDINGS). - In: Machine learning in systems biology : proceedings of the third international workshop, september 5-6, 2009, Ljubljana, Slovenia / [a cura di] S. Dzeroski, P. Geurts, J. Rousu. - Helsinki : Helsinki University Printing House, 2009. - ISBN 9789521056994. - pp. 95-104 (( Intervento presentato al 3rd. convegno Third International Workshop on Machine Learning in Systems Biology tenutosi a Ljubljana, Slovenia nel 2009.
Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction
M. RePrimo
;G. ValentiniUltimo
2009
Abstract
Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data integration techniques for gene function prediction.
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