Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree
- 10.2991/iccia.2012.56How to use a DOI?
- tertiary structure, feature extraction, ECOC, FNT
In this paper we intend to apply a new method to predict tertiary structure. A novel hybrid feature adopted is composed of physicochemical composition (PCC), recurrence quantification analysis (RQA) and pseudo amino acid composition (PseAA). We use the Error Correcting Output Coding (ECOC) based on three flexible neural tree models as the classifiers. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 60.23%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yiming Chen AU - Yuehui Chen PY - 2014/05 DA - 2014/05 TI - Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree BT - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012) PB - Atlantis Press SP - 230 EP - 232 SN - 1951-6851 UR - https://doi.org/10.2991/iccia.2012.56 DO - 10.2991/iccia.2012.56 ID - Chen2014/05 ER -