Publication Type
Report
Version
publishedVersion
Publication Date
7-2010
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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Issue
TR 10-015
First Page
1
Last Page
12
Publisher
University of Minnesota, Department of Computer Science and Engineering
City or Country
Minneapolis, MN
Citation
SHIM, Kyong Jin.
Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition. (2010). 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/1527
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/10-015.pdf