Prediction of Enzyme Classification from Protein Sequence without the Use of Sequence Similarity
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
A statistical framework for genomic data fusion
Bioinformatics
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
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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.