Prediction of protein B-factors using multi-class bounded SVM

Peng Chen; Bing Wang; Hau-San Wong; De-Shuang Huang

doi:10.2174/092986607779816078

Prediction of protein B-factors using multi-class bounded SVM

Peng Chen, Bing Wang, Hau-San Wong, De-Shuang Huang

Department of Computer Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

30 Citations (Scopus)

Abstract

In this paper, we propose the adoption of the bounded support vector machine (BSVM) to predict the B-factors of residues based on a number of distinctive properties of residues. Due to the ability of multi-class classification of the BSVM, we can elaborately distinguish our targets and obtain relatively higher accuracy. © 2007 Bentham Science Publishers Ltd.

Original language	English
Pages (from-to)	185-190
Journal	Protein and Peptide Letters
Volume	14
Issue number	2
DOIs	https://doi.org/10.2174/092986607779816078
Publication status	Published - Feb 2007

Research Keywords

B-factors
Bounded Support Vector Machine
Evolutionary rate
Hydrophobicity profile
Sequence profile

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AU - Wang, Bing

AU - Wong, Hau-San

AU - Huang, De-Shuang

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AB - In this paper, we propose the adoption of the bounded support vector machine (BSVM) to predict the B-factors of residues based on a number of distinctive properties of residues. Due to the ability of multi-class classification of the BSVM, we can elaborately distinguish our targets and obtain relatively higher accuracy. © 2007 Bentham Science Publishers Ltd.

KW - B-factors

KW - Bounded Support Vector Machine

KW - Evolutionary rate

KW - Hydrophobicity profile

KW - Sequence profile

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JO - Protein and Peptide Letters

JF - Protein and Peptide Letters

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ER -

Prediction of protein B-factors using multi-class bounded SVM

Abstract

Research Keywords

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