@inproceedings{scholars6723,
             doi = {10.1109/IJCNN.2016.7727337},
            note = {cited By 0; Conference of 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference Date: 24 July 2016 Through 29 July 2016; Conference Code:124695},
          volume = {2016-O},
           title = {Classification of GPCRs proteins using a statistical encoding method},
            year = {2016},
           pages = {1224--1228},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings of the International Joint Conference on Neural Networks},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007155080&doi=10.1109\%2fIJCNN.2016.7727337&partnerID=40&md5=f4fed208bebf4454354a5acb1caa136f},
        keywords = {Bioinformatics; Encoding (symbols); Proteins, Classification accuracy; Distance-based; G protein coupled receptors; GPCRs; Neural network classifier; Performance measurements; Pharmaceutical industry; Superfamily, Classification (of information)},
        abstract = {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. {\^A}{\copyright} 2016 IEEE.},
          author = {Iqbal, M. J. and Faye, I. and Samir, B. B.},
            isbn = {9781509006199}
}