title: Improved performance in protein secondary structure prediction by inhomogeneous score combination.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
authors.affiliation: LIP, Ecole Normale Supérieure de Lyon, 46, Allée d'Italie, 69364 Lyon cedex 07, France. Yann.Guermeur@ens-lyon.fr
authors.forename: Y
authors.initials: Y
authors.lastname: Guermeur
authors.forename: C
authors.initials: C
authors.lastname: Geourjon
authors.forename: P
authors.initials: P
authors.lastname: Gallinari
authors.forename: G
authors.initials: G
authors.lastname: Deléage
mesh_terms: D000595:Amino Acid Sequence; D003198:Computer Simulation; D016208:Databases, Factual; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D017433:Protein Structure, Secondary