Bioinformatics, Vol 15, 413-421, Copyright © 1999 by Oxford University Press
Y Guermeur, C Geourjon, P Gallinari and G Deleage
MOTIVATION: In many fields of pattern recognition, combination has proved
efficient to increase the generalization performance of individual
prediction methods. Numerous systems have been developed for protein
secondary structure prediction, based on different principles. Finding
better ensemble methods for this task may thus become crucial. Furthermore,
efforts need to be made to help the biologist in the post- processing of
the outputs. RESULTS: An ensemble method has been designed to post-process
the outputs of discriminant models, in order to obtain an improvement in
prediction accuracy while generating class posterior probability estimates.
Experimental results establish that it can increase the recognition rate of
protein secondary structure prediction methods that provide inhomogeneous
scores, even though their individual prediction successes are largely
different. This combination thus constitutes a help for the biologist, who
can use it confidently on top of any set of prediction methods. Moreover,
the resulting estimates can be used in various ways, for instance to
determine which areas in the sequence are predicted with a given level of
reliability. AVAILABILITY: The prediction is freely available over the
Internet on the Network Protein Sequence Analysis (NPS@) WWW server at
http://pbil.ibcp.fr/NPSA/npsa_server.ht ml. The source code of the combiner
can be obtained on request for academic use.
ARTICLES
Improved performance in protein secondary structure prediction by inhomogeneous score combination
LIP, Ecole Normale Superieure de Lyon, 46, Allee d'Italie, 69364 Lyon cedex 07, France. Yann.Guermeur@ens-lyon.fr
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