Predict the Tertiary Structure of Protein with Binary Tree and Ensemble Strategy

DOI

10.2991/icsem.2013.120How to use a DOI?

Keywords

tertiary structure,binary tree, selective ensemble,FNT

Abstract

In this paper we intend to apply a new method to predict tertiary structure. Several feature extraction methods adopted are physicochemical composition, recurrence quantification analysis (RQA) , pseudo amino acid composition (PseAA) and Distance frequency. We construct the binary tree Classification model, and adopt flexible neural tree models as the classifiers. We will train a number of based classifiers through different features extraction methods for every node of binary tree, then employ the selective ensemble method to ensemble them. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 63.58%, 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  - 2013/04
DA  - 2013/04
TI  - Predict the Tertiary Structure of Protein with Binary Tree and Ensemble Strategy
BT  - Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM 2013)
PB  - Atlantis Press
SP  - 603
EP  - 607
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.120
DO  - 10.2991/icsem.2013.120
ID  - Chen2013/04
ER  -