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 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.