Applicability of semi-supervised learning assumptions for gene ontology terms prediction




semi-supervised learning, gene ontology, support vector machines, protein function prediction


Gene Ontology (GO) is one of the most important resources in bioinformatics, aiming to provide a unified framework for the biological annotation of genes and proteins across all species. Predicting GO terms is an essential task for bioinformatics, but the number of available labelled proteins is in several cases insufficient for training reliable machine learning classifiers. Semi-supervised learning methods arise as a powerful solution that explodes the information contained in unlabelled data in order to improve the estimations of traditional supervised approaches. However, semi-supervised learning methods have to make strong assumptions about the nature of the training data and thus, the performance of the predictor is highly dependent on these assumptions. This paper presents an analysis of the applicability of semi-supervised learning assumptions over the specific task of GO terms prediction, focused on providing judgment elements that allow choosing the most suitable tools for specific GO terms. The results show that semi-supervised approaches significantly outperform the traditional supervised methods and that the highest performances are reached when applying the cluster assumption. Besides, it is experimentally demonstrated that cluster and manifold assumptions are complementary to each other and an analysis of which GO terms can be more prone to be correctly predicted with each assumption, is provided.

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Author Biographies

Jorge Alberto Jaramillo-Garzón, Metropolitan Technological Institute

Assistant Professor. Group of Automation, Electronics and Computational Sciences, Faculty of Engineering.

César Germán Castellanos-Domínguez, National University of Colombia

Titular Professor, Electronics and Computational Sciences department, Faculty of Engineering.

Alexandre Perera-Lluna, Polytechnic University of Catalonia

Biomedical Engineering Research Center.


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How to Cite

Jaramillo-Garzón, J. A., Castellanos-Domínguez, C. G., & Perera-Lluna, A. (2016). Applicability of semi-supervised learning assumptions for gene ontology terms prediction. Revista Facultad De Ingeniería Universidad De Antioquia, (79), 19–32.

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