A voting scheme to improve the secondary structure prediction

Authors:
Javid Taheri;Albert Y. Zomaya
Affiliations:
School of Information Technologies, J12, The University of Sydney, NSW 2006, Australia;School of Information Technologies, J12, The University of Sydney, NSW 2006, Australia
Venue:
AICCSA '10 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010
Year:
2010

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A resilient voting scheme for improving secondary structure prediction

MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence

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Abstract

This paper presents a novel approach, namely SSVS, to improve the secondary structure prediction of proteins. In this work, a Radial Basis Function Neural Network is trained to combine different answers found by different secondary structure prediction techniques to produce superior answers. 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.