The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Protein secondary structure prediction with a neural network.
Open accessAbstract
A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum overall predictive accuracy of 63% for three states: helix, sheet, and coil. A numerical measure of helix and sheet tendency for each residue was obtained from the calculations. When predictions were filtered to include only the strongest 31% of predictions, the predictive accuracy rose to 79%.
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Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399
Cited by
Combining evolutionary information and neural networks to predict protein secondary structure
Rost, B., & Sander, C. (1994) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340190108 · Cited by 1,452
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781
Gasteiger, J., & Zupan, J. (1993) · Angewandte Chemie International Edition in English · doi:10.1002/anie.199305031 · Cited by 503
Deep learning in biomedical data science
Baldi, P. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013343 · Cited by 119
Beyreuther, S., Hunger, J., Huttner, G., Mann, S., & Zsolnai, L. (1996) · Chemische Berichte · doi:10.1002/cber.19961290704 · Cited by 32
Metadata
- Authors
- Holley, L. H., & Karplus, M.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1989-01
- Volume
- 86
- Issue
- 1
- Pages
- 152-156
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2057157558
- Cited by
- 504
- References
- 3
- FWCI
- 11.90
- Type
- journal article
- Language
- en
- Open access
- green
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- Holley, L. H., & Karplus, M. (1989). Protein secondary structure prediction with a neural network. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.86.1.152
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
