PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction
Matteo Re and Giorgio Valentini
Journal of Machine Learning Research, W&C Proceedings Volume 8, pp. 98-111, 2010.

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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Multimodal Integration
ID Code:6298
Deposited By:Giorgio Valentini
Deposited On:08 March 2010