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

[ Vol. 17 , Issue. 5 ]


Quan Gu, Yong-Sheng Ding and Tong-Liang Zhang   Pages 559 - 567 ( 9 )


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.


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


College of Information Sciences and Technology, Donghua University, Shanghai 201620, P.R. China.

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