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ASME Press Select Proceedings
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Editor
ISBN-10:
0791802655
No. of Pages:
650
Publisher:
ASME Press
Publication date:
2007
eBook Chapter
8 Protein Secondary Structure Prediction with Hydrophobicity and Hydrophobic Moment
By
Tzu-Cheng Chuang
,
Tzu-Cheng Chuang
School of Electrical and Computer Engineering,
Purdue University
, West Lafayette, Indiana 47907
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Saul B. Gelfand
,
Saul B. Gelfand
School of Electrical and Computer Engineering,
Purdue University
, West Lafayette, Indiana 47907
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Okan K. Ersoy
Okan K. Ersoy
School of Electrical and Computer Engineering,
Purdue University
, West Lafayette, Indiana 47907
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Page Count:
8
-
Published:2007
Citation
Chuang, T, Gelfand, SB, & Ersoy, OK. "Protein Secondary Structure Prediction with Hydrophobicity and Hydrophobic Moment." Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17. Ed. Dagli, CH. ASME Press, 2007.
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Protein secondary structure prediction has been satisfactorily performed by machine learning techniques such as support vector machines (SVM's). We discuss a special technique to include hyrophobicity information to further improve the classification results. Hydrophobicity or hydrophobic moment measure of each amino acid is included within a given window length in the protein secondary structure prediction using support vector machines. The input data is divided into two groups, which is subsequently classified by an SVM. By including hydrophobicity or hydrophobic moment, the classification accuracy is increased. Comparing the accuracy between using 1 SVM and 2 SVMs. 2 SVMs method has 3–9%...
Abstract
Introduction
Dataset
Hydrophobicity and Hydrophobic Moment
Input Coding Method
Support Vector Machines
Experiments and Results
Discussion
Conclusions
Acknowledgement
References
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