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

Quan      Gu; Yong-Sheng      Ding; Tong-Liang      Zhang

doi:10.2174/092986610791112693

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.

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

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Protein & Peptide Letters

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

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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.

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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