Abstract
The aim of secondary structure prediction is to locate all α-helices and β- strands within a protein. We view the problem of secondary structure prediction as a statistical pattern recognition problem. We particularly focus on the sta- tistical analysis of the problem data. A thorough statistical data analysis is inevitable in order to design an appropriate classifier. We describe classifiers based on nearest neighbors, consensus, and neural networks.
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© 2003 Springer Science+Business Media New York
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Zimmermann, KH. (2003). Secondary Structure Prediction. In: An Introduction to Protein Informatics. The Kluwer International Series in Engineering and Computer Science, vol 749. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9210-9_6
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DOI: https://doi.org/10.1007/978-1-4419-9210-9_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-4839-9
Online ISBN: 978-1-4419-9210-9
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