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Fuzz-SSVS: A Fuzzy logic based voting scheme to improve protein secondary structure prediction

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4 Author(s)
Taheri, J. ; Centre for Distrib. & High Performance Comput., Univ. of Sydney, Sydney, NSW, Australia ; Zomaya, A.Y. ; Delicato, F.C. ; Pires, P.F.

This paper presents a novel approach, Fuzz-SSVS, to improve the secondary structure prediction of proteins. In this work, a Sugeno based Fuzzy System is trained to act as a voting system to combine results of several secondary structure prediction techniques and produce superior answers. Fuzz-SSVS is tested with three of the well-known benchmarks in this field. The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.

Published in:

Computer Systems and Applications (AICCSA), 2011 9th IEEE/ACS International Conference on

Date of Conference:

27-30 Dec. 2011