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