Skip to Main Content
Skip Nav Destination
ASME Press Select Proceedings

Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17

Editor
C. H. Dagli
C. H. Dagli
Search for other works by this author on:
ISBN-10:
0791802655
No. of Pages:
650
Publisher:
ASME Press
Publication date:
2007

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% higher accuracy than 1 SVM method.

This content is only available via PDF.
You do not currently have access to this chapter.
Close Modal

or Create an Account

Close Modal
Close Modal