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

2010/04/14 by Quan Gu, Yongsheng Ding, Tong-Liang Zhang · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Machine Learning in Bioinformatics #Computational Drug Discovery Methods #Receptor Mechanisms and Signaling

paper · doi:10.2174/092986610791112693

openalex publication_date 2010/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

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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• G protein-coupled receptor [wikipedia]

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