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Top-down hierarchical ensembles of classifiers for predicting G-Protein-Coupled-Receptor functions

Costa, Eduardo P. and Lorena, Ana C. and Carvalho, Andre C. P. L. F. and Freitas, Alex A. (2008) Top-down hierarchical ensembles of classifiers for predicting G-Protein-Coupled-Receptor functions. In: Bazzan, Ana L.C. and Craven, Mark and Martins, Natalia F., eds. Advances in Bioinformatics and Computational Biology Third Brazilian Symposium on Bioinformatics. Springer, Berlin, Germany, pp. 35-46. ISBN 978-3-540-85556-9. E-ISBN 978-3-540-85557-6. (doi:10.1007/978-3-540-85557-6_4) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:24052)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/978-3-540-85557-6_4

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.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-540-85557-6_4
Uncontrolled keywords: classification, bioinformatics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:12 UTC
Last Modified: 16 Feb 2021 12:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/24052 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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