Prediction of Gene Function Using Ensembles of SVMs and Heterogeneous Data Sources

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.