DocumentCode
2338266
Title
A voting scheme to improve the secondary structure prediction
Author
Taheri, Javid ; Zomaya, Albert Y.
Author_Institution
Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia
fYear
2010
fDate
16-19 May 2010
Firstpage
1
Lastpage
7
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.
Keywords
biology computing; proteins; radial basis function networks; protein; radial basis function neural network; secondary structure prediction; voting scheme; Accuracy; Amino acids; Artificial neural networks; Benchmark testing; Periodic structures; Prediction algorithms; Proteins; Radial Basis Function Neural Networks; Secondary Structure Prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Systems and Applications (AICCSA), 2010 IEEE/ACS International Conference on
Conference_Location
Hammamet
Print_ISBN
978-1-4244-7716-6
Type
conf
DOI
10.1109/AICCSA.2010.5586931
Filename
5586931
Link To Document