PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Integration of heterogeneous data sources for gene function prediction using Decision Templates and ensembles of learning machines
Matteo Re and Giorgio Valentini
Neurocomputing Volume 73, Number 7-9, pp. 1533-1537, 2010.


Several solutions have been proposed to exploit the availability of heterogeneous sources of biomolecular data for gene function prediction, but few attention has been dedicated to the evaluation of the potential improvement in functional classification results that could be achieved through data fusion realized by means of ensemble-based techniques. In this contribution we test the performance of several ensembles of Support Vector Machine (SVM) classifiers, in which each component learner has been trained on different types of bio-molecular data, and then combined to obtain a consensus prediction using different aggregation techniques. Experimental results using data obtained with different high-throughput biotechnologies show that simple ensemble methods outperform both learning machines trained on single homogeneous types of bio-molecular data, and vector space integration methods.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Multimodal Integration
ID Code:6302
Deposited By:Giorgio Valentini
Deposited On:08 March 2010