Abstract
In this paper, we present a new hypersphere machine learning method and use it to predict all protein secondary structures. It finds sequences with sufficiently high homology. Prediction accuracy of the new method with protein secondary structures was good (average 89.3%). However, the method could not classify all test cases.
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© 2001 Springer-Verlag Berlin Heidelberg
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Siermala, M. (2001). Prediction of Protein Secondary Structures of All Types Using New Hypersphere Machine Learning Method. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_16
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DOI: https://doi.org/10.1007/3-540-48229-6_16
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42294-5
Online ISBN: 978-3-540-48229-1
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