Abstract
G-protein coupled receptors (GPCRs) are involved in various physiological processes. Therefore, classification of amine type GPCRs is important for proper understanding of their functions. Though some effective methods have been developed, it still remains unknown how many and which features are essential for this task. Empirical studies show that feature selection might address this problem and provide us with some biologically useful knowledge. In this paper, a feature selection technique is introduced to identify those relevant features of proteins which are potentially important for the prediction of amine type GPCRs. The selected features are finally accepted to characterize proteins in a more compact form. High prediction accuracy is observed on two data sets with different sequence similarity by 5-fold cross-validation test. The comparison with a previous method demonstrates the efficiency and effectiveness of the proposed method.
Keywords: G-protein coupled receptor, Feature selection, Protein classification, Support vector machine
Protein & Peptide Letters
Title: Classification of Amine Type G-Protein Coupled Receptors with Feature Selection
Volume: 15 Issue: 8
Author(s): Qing-Bin Gao, Cheng Wu, Xiu-Qiang Ma, Jian Lu and Jia He
Affiliation:
Keywords: G-protein coupled receptor, Feature selection, Protein classification, Support vector machine
Abstract: G-protein coupled receptors (GPCRs) are involved in various physiological processes. Therefore, classification of amine type GPCRs is important for proper understanding of their functions. Though some effective methods have been developed, it still remains unknown how many and which features are essential for this task. Empirical studies show that feature selection might address this problem and provide us with some biologically useful knowledge. In this paper, a feature selection technique is introduced to identify those relevant features of proteins which are potentially important for the prediction of amine type GPCRs. The selected features are finally accepted to characterize proteins in a more compact form. High prediction accuracy is observed on two data sets with different sequence similarity by 5-fold cross-validation test. The comparison with a previous method demonstrates the efficiency and effectiveness of the proposed method.
Export Options
About this article
Cite this article as:
Gao Qing-Bin, Wu Cheng, Ma Xiu-Qiang, Lu Jian and He Jia, Classification of Amine Type G-Protein Coupled Receptors with Feature Selection, Protein & Peptide Letters 2008; 15 (8) . https://dx.doi.org/10.2174/092986608785203755
DOI https://dx.doi.org/10.2174/092986608785203755 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |

- Author Guidelines
- Bentham Author Support Services (BASS)
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers