An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species
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Deborah Galpert, Sara del Río, Francisco Herrera, Evys Ancede-Gallardo, Agostinho Antunes, Guillermin Agüero-Chapin, "An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species", BioMed Research International, vol. 2015, Article ID 748681, 12 pages, 2015. https://doi.org/10.1155/2015/748681
SponsorshipPortuguese Foundation for Science and Technology SFRH/BPD/92978/2013; European Union (EU); national funds through FCT PEst-C/MAR/LA0015/2013 PTDC/AAC-AMB/121301/2010 FCOMP-01-0124-FEDER-019490; Spanish Government TIN2014-57251-P; Regional Andalusian Research P11-TIC-7765 P10-TIC-6858
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 cerevisiaeSchizosaccharomyces 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.