Optimizing amino acid groupings for GPCR classification

Davies, Matthew N. and Secker, Andrew D. and Freitas, Alex A. and Clark, Edward and Timmis, Jon and Flower, Darren R. (2008) Optimizing amino acid groupings for GPCR classification. Bioinformatics, 24 (18). pp. 1980-1986. ISSN 1367-4803. (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)

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Motivation: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. Results: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

Item Type: Article
Uncontrolled keywords: classification, clustering, data mining, bioinformatics
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: Mark Wheadon
Date Deposited: 29 Mar 2010 12:12
Last Modified: 19 May 2014 15:56
Resource URI: https://kar.kent.ac.uk/id/eprint/24049 (The current URI for this page, for reference purposes)
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