Title

Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition

Publication Type

Conference Proceeding Article

Publication Date

2010

Abstract

Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone.

Keywords

backbone alphabet, fold recognition, local structure, protein backbone

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

IEEE International Conference on Data Mining Workshops (ICDM-10): Workshop on Biological Data Mining and its Applications in Healthcare

First Page

755

Last Page

762

ISBN

9780769542577

Identifier

10.1109/ICDMW.2010.168

Publisher

IEEE

City or Country

Sydney, NSW, Australia

Additional URL

http://doi.ieeecomputersociety.org/10.1109/ICDMW.2010.168