Referenced Items are not available for this document.
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.
Published in:
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Date of Conference: 13-13 Dec. 2010
- Page(s):
- 755 - 762
- E-ISBN :
- 978-0-7695-4257-7
- Print ISBN:
- 978-1-4244-9244-2
- INSPEC Accession Number:
- 11791642
- Conference Location :
- Sydney, NSW
- Digital Object Identifier :
- 10.1109/ICDMW.2010.168
- Product Type:
- Conference Publications
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