Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into chou's general pseaac

Highlights • In this paper the protein samples are represented by eight sequence derived properties • In this paper the amino acid composition, dipeptide composition, correlation features, composition, transition, distribution, sequence order descriptors and pseudo amino acid composition with total 1497 number of sequence derived features are used • Here a weighted k –nearest neighbor classifier are proposed to use with UNION of best 50 features selected by Fisher score based feature selection, ReliefF, fast correlation based filter, minimum redundancy maximum relevancy and support vector machine based recursive feature elimination feature selection methods to exploit the advantages of these feature selection methods.