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
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;
Conference_Titel :
Computer Systems and Applications (AICCSA), 2010 IEEE/ACS International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4244-7716-6
DOI :
10.1109/AICCSA.2010.5586931
