TY - GEN
T1 - Prediction of subcelluar localization using maximal-margin spherical support vector machine
AU - Chen, Wei Ming
AU - Wu, I. Lin
AU - Chiang, Jung Hsien
AU - Hao, Pei Yi
PY - 2010
Y1 - 2010
N2 - Prediction of subcellular localization of various proteins is an important and well-studied problem. Each compartment in cell has specific tasks, and proteins in each compartment are synthesized to fulfill these tasks, and for this reason, an effective predictive system for protein subcellular localization is crucial. Therefore, we propose a prediction based on maximal margin sphere-structure multi-class support vector, and use some different types of composition in amino acid for features. The experimental results show that the proposed method is better than transitional support vector machine.
AB - Prediction of subcellular localization of various proteins is an important and well-studied problem. Each compartment in cell has specific tasks, and proteins in each compartment are synthesized to fulfill these tasks, and for this reason, an effective predictive system for protein subcellular localization is crucial. Therefore, we propose a prediction based on maximal margin sphere-structure multi-class support vector, and use some different types of composition in amino acid for features. The experimental results show that the proposed method is better than transitional support vector machine.
UR - http://www.scopus.com/inward/record.url?scp=78149328401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149328401&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2010.5580840
DO - 10.1109/ICMLC.2010.5580840
M3 - Conference contribution
AN - SCOPUS:78149328401
SN - 9781424465262
T3 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
SP - 1476
EP - 1481
BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Y2 - 11 July 2010 through 14 July 2010
ER -