Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree

DOI

10.2991/iccia.2012.56How to use a DOI?

Keywords

tertiary structure, feature extraction, ECOC, FNT

Abstract

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.

Copyright

© 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/).

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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  -