Abstract
Ortholog detection should be improved because of the real value of ortholog genes in the prediction of protein functions. Datasets in the binary classification problem can be represented as information systems. We use a gene pair extended similarity relation based on an extension of the Rough Set Theory and aggregated gene similarity measures as gene features, to select feature subsets with the aid of quality measures that take imbalance into account. The proposed procedure can be useful for datasets with few features and discrete parameters. The case reduction obtained from the approximation of ortholog and non-ortholog concepts might be an effective method to cope with extremely high imbalance in supervised classification.
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Galpert Cańizares, D., Millo Sánchez, R., García Lorenzo, M.M., Casas Cardoso, G., Grau Abalo, R., García, L.A. (2014). Rough Sets in Ortholog Gene Detection. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_15
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DOI: https://doi.org/10.1007/978-3-319-08729-0_15
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