A Novel Algorithm for Predicting '-barrel outer Membrane Proteins using ACO-based Hyper-parameter Selection for LS-SVMs
Guangming Xian, Biqing-Zeng Xian
Available Online November 2012.
- https://doi.org/10.2991/citcs.2012.51How to use a DOI?
- transmembrane ' strands; LSSVM; ACO; prediction of membrane protein; parameters Optimization
- 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.
- 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 -