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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | King, R. D. Ouali, M. |
| Description | Country affiliation: United kingdom Author Affiliation: Ouali M ( Department of Computer Science, University of Wales, Ceredigion, United Kingdom. mho@aber.ac.uk) |
| Abstract | We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and J.A. Cuff). This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it 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 only a local window and resampling techniques. This indicates that the importance of long-range interactions for the prediction of beta-strands has been probably previously overestimated. |
| ISSN | 09618368 |
| e-ISSN | 1469896X |
| Journal | Protein Science |
| Issue Number | 6 |
| Volume Number | 9 |
| Language | English |
| Publisher | Wiley-Blackwell (on behalf of The Protein Society) |
| Publisher Date | 2000-06-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Protein Structure, Secondary Proteins Chemistry Neural Networks (computer) Research Support, Non-u.s. Gov't Discipline Biochemistry |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine Molecular Biology Biochemistry |
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