title: Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns.
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.
authors: College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
authors: Q
authors: Q
authors: Gu
authors: Y S
authors: YS
authors: Ding
authors: T L
authors: TL
authors: Zhang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis*; D000818:Animals; D002417:Cattle; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D019277:Entropy; D057927:Hydrophobic and Hydrophilic Interactions; D008969:Molecular Sequence Data; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000145:classification; D012243:Rhodopsin / Q000737:chemistry
chemical_list: D000596:Amino Acids; D043562:Receptors, G-Protein-Coupled; D012243:Rhodopsin