Abstract
Protein fold recognition is an important problem in molecular biology. Machine learning symbolic approaches have been applied to automatically discover local structural signatures and relate these to the concept of fold in SCOP. However, most of these methods cannot handle uncertainty being therefore not able to solve multiple prediction problems. In this paper we present an application of the symbolic-statistical framework PRISM to a multi-class protein fold recognition problem. We compare the proposed approach to a symbolic-only technique and show that the hybrid framework outperforms the symbolic-only one in terms of predictive accuracy in the multiple prediction problem.
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© 2007 Springer-Verlag Berlin Heidelberg
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Biba, M., Esposito, F., Ferilli, S., Basile, T.M.A., Di Mauro, N. (2007). Multi-class Protein Fold Recognition Through a Symbolic-Statistical Framework. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_85
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DOI: https://doi.org/10.1007/978-3-540-73400-0_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73399-7
Online ISBN: 978-3-540-73400-0
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