Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chous Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns

Title: Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chous Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns

Volume: 17 Issue: 5

Author(s): Quan Gu, Yong-Sheng Ding and Tong-Liang Zhang

Affiliation:

Keywords: G-protein-coupled receptors, low homology, pseudo amino acid, approximate entropy, hydrophobicity patterns, AdaBoost

Abstract: We use approximate entropy and hydrophobicity patterns to predict G-protein-coupled receptors. Adaboost classifier is adopted as the prediction engine. A low homology dataset is used to validate the proposed method. Compared with the results reported, the successful rate is encouraging. The source code is written by Matlab.

Cite this article as:

Gu Quan, Ding Yong-Sheng and Zhang Tong-Liang, Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chous Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns, Protein & Peptide Letters 2010; 17 (5) . https://dx.doi.org/10.2174/092986610791112693

DOI https://dx.doi.org/10.2174/092986610791112693	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305