Proceedings of the 2012 National Conference on Information Technology and Computer Science

A Novel Algorithm for Predicting '-barrel outer Membrane Proteins using ACO-based Hyper-parameter Selection for LS-SVMs

Authors
Guangming Xian, Biqing-Zeng Xian
Corresponding Author
Guangming Xian
Available Online November 2012.
DOI
https://doi.org/10.2991/citcs.2012.51How to use a DOI?
Keywords
transmembrane ' strands; LSSVM; ACO; prediction of membrane protein; parameters Optimization
Abstract
An ACO-based hyper-parameter selection for least squares support vector machines (LS-SVMs) was trained to predict the topology of transmembrane ' strands proteins. It should be stressed that it is very important to do a careful model selection of the tuning parameters for LS-SVM. In this paper, a novel hyper-parameter selection method for LS-SVMs is presented based on the ant clony optimization (ACO). Optimal LS-SVMs parameters for RBF kernel are selected to predict the topology of the transmembrane ' strands proteins. The feasibility of this method is examined on one test database set. For the testing database, the present LS-SVMs method with RBF kernel predicts higher accuracy than SVM and HMM method. The simulation result shows that this prediction model for transmembrane ' strands proteins is accurate.
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Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.51How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Guangming Xian
AU  - Biqing-Zeng Xian
PY  - 2012/11
DA  - 2012/11
TI  - A Novel Algorithm for Predicting '-barrel outer Membrane Proteins using ACO-based Hyper-parameter Selection for LS-SVMs
BT  - 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 188
EP  - 191
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.51
DO  - https://doi.org/10.2991/citcs.2012.51
ID  - Xian2012/11
ER  -