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Bioinformatics Advance Access originally published online on November 7, 2006
Bioinformatics 2006 22(23):2865-2869; doi:10.1093/bioinformatics/btl512
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Application of a simple likelihood ratio approximant to protein sequence classification

László Kaján 1,{dagger}, Attila Kertész-Farkas 2, Dino Franklin 1, Neli Ivanova 1, András Kocsor 2 and Sándor Pongor 1,3,*

1 Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy
2 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1. H-6720 Szeged, Hungary
3 Bioinformatics Group, Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62 H-6701 Szeged, Hungary

*To whom correspondence should be addressed.

Motivation: Likelihood ratio approximants (LRA) have been widely used for model comparison in statistics. The present study was undertaken in order to explore their utility as a scoring (ranking) function in the classification of protein sequences.

Results: We used a simple LRA-based on the maximal similarity (or minimal distance) scores of the two top ranking sequence classes. The scoring methods (Smith–Waterman, BLAST, local alignment kernel and compression based distances) were compared on datasets designed to test sequence similarities between proteins distantly related in terms of structure or evolution. It was found that LRA-based scoring can significantly outperform simple scoring methods.

Contact: pongor{at}icgeb.org.

Supplementary information: http://www.inf.u-szeged.hu/~kfa/lra06/.

Received on April 19, 2006; revised on October 3, 2006; accepted on October 3, 2006

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