Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2010
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 Science and Engineering
Publication
10th IEEE International Conference on Data Mining Workshops ICDMW 2010: Proceedings, Sydney, Australia, 14-17 December
First Page
755
Last Page
762
ISBN
9780769542577
Identifier
10.1109/ICDMW.2010.168
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
SHIM, Kyong Jin.
Evaluation of Protein Backbone Alphabets : Using Predicted Local Structure for Fold Recognition. (2010). 10th IEEE International Conference on Data Mining Workshops ICDMW 2010: Proceedings, Sydney, Australia, 14-17 December. 755-762.
Available at: https://ink.library.smu.edu.sg/sis_research/1505
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.ieeecomputersociety.org/10.1109/ICDMW.2010.168