Prediction of subcelluar localization using maximal-margin spherical support vector machine

Wei Ming Chen, I. Lin Wu, Jung-Hsien Chiang, Pei Yi Hao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages1476-1481
Number of pages6
DOIs
Publication statusPublished - 2010 Nov 15
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 2010 Jul 112010 Jul 14

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume3

Other

Other2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
CountryChina
CityQingdao
Period10-07-1110-07-14

Fingerprint

Support vector machines
Proteins
Amino acids
Chemical analysis

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Human-Computer Interaction

Cite this

Chen, W. M., Wu, I. L., Chiang, J-H., & Hao, P. Y. (2010). Prediction of subcelluar localization using maximal-margin spherical support vector machine. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 (pp. 1476-1481). [5580840] (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 3). https://doi.org/10.1109/ICMLC.2010.5580840
Chen, Wei Ming ; Wu, I. Lin ; Chiang, Jung-Hsien ; Hao, Pei Yi. / Prediction of subcelluar localization using maximal-margin spherical support vector machine. 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. pp. 1476-1481 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010).
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Chen, WM, Wu, IL, Chiang, J-H & Hao, PY 2010, Prediction of subcelluar localization using maximal-margin spherical support vector machine. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010., 5580840, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, vol. 3, pp. 1476-1481, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, 10-07-11. https://doi.org/10.1109/ICMLC.2010.5580840

Prediction of subcelluar localization using maximal-margin spherical support vector machine. / Chen, Wei Ming; Wu, I. Lin; Chiang, Jung-Hsien; Hao, Pei Yi.

2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. p. 1476-1481 5580840 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

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Chen WM, Wu IL, Chiang J-H, Hao PY. Prediction of subcelluar localization using maximal-margin spherical support vector machine. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. p. 1476-1481. 5580840. (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010). https://doi.org/10.1109/ICMLC.2010.5580840