relation: https://khub.utp.edu.my/scholars/6723/
title: Classification of GPCRs proteins using a statistical encoding method
creator: Iqbal, M.J.
creator: Faye, I.
creator: Samir, B.B.
description: Classification of G protein-coupled receptors (GPCRs) according to their functions is an ongoing area of research which is helpful for the pharmaceutical industry in the development of drug targets for major diseases. Currently, more than 40 drugs in the market target GPCRs. The experimental methods of determining their function are very expensive and time consuming. Due to a rapid and constant increase in the GPCRs proteins in the public databases, it is extremely important to develop computational techniques that lessen the gap between the sequenced proteins and proteins with known functions. In this paper, a statistical method was utilized to encode proteins sequences. The encoding technique considers various distances for an amino acid in a sequence at different levels of decompositions. The Neural Network and Support Vector Machines classifiers were compared on 2 well-known GPCRs datasets. The results showed that better performance is achieved using neural network classifier. The classification accuracies were in the range of 94 to 98. © 2016 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Iqbal, M.J. and Faye, I. and Samir, B.B.  (2016) Classification of GPCRs proteins using a statistical encoding method.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007155080&doi=10.1109%2fIJCNN.2016.7727337&partnerID=40&md5=f4fed208bebf4454354a5acb1caa136f
relation: 10.1109/IJCNN.2016.7727337
identifier: 10.1109/IJCNN.2016.7727337