Abstract
Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. An approach that can be used in the prediction of a protein function consists of searching against secondary databases, also known as signature databases. Different strategies can be applied to use protein signatures in the prediction of function of proteins. A sophisticated approach consists of inducing a classification model for this prediction. This paper applies five hierarchical classification methods based on the standard Top-Down approach and one hierarchical classification method based on a new approach named Top-Down Ensembles - based on the hierarchical combination of classifiers - to three different protein functional classification datasets that employ protein signatures. The algorithm based on the Top-Down Ensembles approach presented slightly better results than the other algorithms, indicating that combinations of classifiers can improve the performance of hierarchical classification models.
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Costa, E.P., Lorena, A.C., Carvalho, A.C.P.L.F., Freitas, A.A. (2008). Top-Down Hierarchical Ensembles of Classifiers for Predicting G-Protein-Coupled-Receptor Functions. In: Bazzan, A.L.C., Craven, M., Martins, N.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2008. Lecture Notes in Computer Science(), vol 5167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85557-6_4
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DOI: https://doi.org/10.1007/978-3-540-85557-6_4
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