Application of a simple likelihood ratio approximant to protein sequence classification

Authors:
László Kaján;Attila Kertész-Farkas;Dino Franklin;Neli Ivanova;András Kocsor;Sándor Pongor
Affiliations:
Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy;Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1. H-6720 Szeged, Hungary;Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy;Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy;Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1. H-6720 Szeged, Hungary;Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy
Venue:
Bioinformatics
Year:
2006

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Abstract

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