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Protein & Peptide Letters
ISSN (Print): 0929-8665
ISSN (Online): 1875-5305
VOLUME: 17
ISSUE: 5
DOI: 10.2174/092986610791112693      Price:  $58









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

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Author(s): Quan Gu, Yong-Sheng Ding and Tong-Liang Zhang
Pages 559-567 (9)
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
Affiliation:
College of Information Sciences and Technology, Donghua University, Shanghai 201620, P.R. China.