Cascaded multiple classifiers for secondary structure prediction

Authors
Citation
M. Ouali et Rd. King, Cascaded multiple classifiers for secondary structure prediction, PROTEIN SCI, 9(6), 2000, pp. 1162-1176
Citations number
71
Categorie Soggetti
Biochemistry & Biophysics
Journal title
PROTEIN SCIENCE
ISSN journal
09618368 → ACNP
Volume
9
Issue
6
Year of publication
2000
Pages
1162 - 1176
Database
ISI
SICI code
0961-8368(200006)9:6<1162:CMCFSS>2.0.ZU;2-6
Abstract
We describe a neu classifier for protein secondary structure prediction tha t is formed by cascading together different types of classifiers using neur al networks and linear discrimination. The new classifier achieves an accur acy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new no nredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barto n and J.A. Cuff). This database was especially designed to train and test p rotein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that i t is possible to design classifiers that can highly discriminate the three classes (H, E, C) with an accuracy of up to 78% for beta-strands, using onl y a local window and resampling techniques. This indicates that the importa nce ut long-range interactions for the prediction of beta-strands has been probably previously overestimated.