An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction.

Otero, Fernando E.B. and Segond, Marc and Freitas, Alex A. and Johnson, Colin G. and Robilliard, Denis and Fonlupt, Cyril (2009) An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction. In: Abraham, Ajith and Hassanien, Aboul-Ella and Snasel, Vaclav, eds. Studies in Computational Intelligence. Studies in Computational Intelligence 205, Founda . Springer, Berlin, pp. 339-357. ISBN 3642015352. (doi:10.1007/978-3-642-01536-6_13) (Full text available)

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Abstract

Many classification schemes for defining protein functions, such as Gene Ontology (GO), are organised in a hierarchical structure. Nodes near the root of the hierarchy represent general functions while nodes near the leaves of the hierarchy represent more specific functions, giving the flexibility to specify at which level the protein will be annotated. In a data mining perspective, hierarchical structures present a more challenging problem, since the relationship between nodes need to be considered. This chapter presents an empirical evaluation of different protein representations for protein function prediction in terms of maximizing predictive accuracy, investigating which type of representation is more suitable for different levels of the GO hierarchy.

Item Type: Book section
Uncontrolled keywords: data mining, bioinformatics, classification, protein function
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Fernando Otero
Date Deposited: 29 Mar 2010 12:16
Last Modified: 22 Feb 2016 10:47
Resource URI: https://kar.kent.ac.uk/id/eprint/24127 (The current URI for this page, for reference purposes)
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