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BioMed Research International
Volume 2015 (2015), Article ID 748681, 12 pages
Research Article

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

1Departamento de Ciencias de la Computación, Universidad Central “Marta Abreu” de Las Villas (UCLV), 54830 Santa Clara, Cuba
2Department of Computer Science and Artificial Intelligence, Research Center on Information and Communications Technology (CITIC-UGR), University of Granada, 18071 Granada, Spain
3Centro de Bioactivos Químicos, Universidad Central “Marta Abreu” de Las Villas (UCLV), 54830 Santa Clara, Cuba
4Centro Interdisciplinar de Investigação Marinha e Ambiental (CIMAR/CIIMAR), Universidade do Porto, Rua dos Bragas 177, 4050-123 Porto, Portugal
5Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal

Received 7 April 2015; Revised 26 July 2015; Accepted 20 August 2015

Academic Editor: Shigehiko Kanaya

Copyright © 2015 Deborah Galpert et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.