Multi-class Protein Fold Recognition Through a Symbolic-Statistical Framework

Authors:
Marenglen Biba;Floriana Esposito;Stefano Ferilli;Teresa M. Basile;Nicola Mauro
Affiliations:
Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy;Department of Computer Science, University of Bari, Italy
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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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.