Prediction of GPCRs with pseudo @entity535 composition: employing composite features and XXXX based classification.

@entity535 @entity11144 @entity24101
@entity24101 ( @entity24101 ) is a protein family that is found only in the Eukaryotes. They are used for the interfacing of cell to the outside world and are involved in many physiological processes. Their role in drug development is evident. Hence, the prediction of GPCRs is very much demanding. Because of the unavailability of 3D structures of most of the GPCRs; the statistical and machine learning based prediction of GPCRs is much demanding. The GPCRs are classified into family, sub family and sub-sub family levels in the proposed approach. We have extracted features using the hybrid combination of Pseudo @entity535 , Fast Fourier Transform and Split @entity535 techniques. The overall feature vector is then reduced using Principle component analysis. Mostly, GPCRs are composed of two or more sub units. The arrangement and number of sub units forming a @entity24101 are referred to as quaternary structure. The functions of GPCRs are closely related to their quaternary structure. The classification in the present research is performed using @entity11144 ( @entity11144 ) measure, which can efficiently analyze the numerical relation between various components of GPCRs. The @entity11144 measure based classification has shown remarkable improvement in predicting GPCRs.
