The ever increasing amount of biomolecular data available in public domain databases for a broad range of organisms coupled with recent advances in machine learning research has stimulated interest in computational approaches on gene function prediction. In this context data integration from heterogeneous biomolecular data sources plays a key role. In this contribution we test the performance of several ensembles of SVM classifiers, in which each component learner has been trained on different types of data, and then combined using different aggregation techniques. The compared combination methods are the widely adopted linear weighted combination, the logarithmic weighted combination and the similarity based decision templates approach. The results show that heterogeneous data integration through ensemble methods represents a valuable research line in gene function prediction.
Prediction of gene function using ensembles of SVMs and heterogeneous data sources / M. Re, G. Valentini (STUDIES IN COMPUTATIONAL INTELLIGENCE). - In: Applications of supervised and unsupervised ensemble methods / [a cura di] O. Okun, G. Valentini. - Berlin : Springer, 2009. - ISBN 9783642039980. - pp. 79-91 [10.1007/978-3-642-03999-7_5]
Prediction of gene function using ensembles of SVMs and heterogeneous data sources
M. RePrimo
;G. ValentiniUltimo
2009
Abstract
The ever increasing amount of biomolecular data available in public domain databases for a broad range of organisms coupled with recent advances in machine learning research has stimulated interest in computational approaches on gene function prediction. In this context data integration from heterogeneous biomolecular data sources plays a key role. In this contribution we test the performance of several ensembles of SVM classifiers, in which each component learner has been trained on different types of data, and then combined using different aggregation techniques. The compared combination methods are the widely adopted linear weighted combination, the logarithmic weighted combination and the similarity based decision templates approach. The results show that heterogeneous data integration through ensemble methods represents a valuable research line in gene function prediction.Pubblicazioni consigliate
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