An Efficient Approach for the Prediction of G-Protein Coupled Receptors and Their Subfamilies

  • Arvind Kumar Tiwari
  • Rajeev Srivastava
  • Subodh Srivastava
  • Shailendra Tiwari
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 44)


G-protein coupled receptors are responsible for many physiochemical processes such as neurotransmission, metabolism, cellular growth and immune response. So it necessary to design a robust and efficient approach for the prediction of G-protein coupled receptors their subfamilies. To address the issue of efficient classification G-protein coupled receptors and their subfamilies, here in this paper we propose to use a weighted k-nearest neighbor classifier 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. The proposed method achieved an overall accuracy of 99.9, 98.3 % MCC values of 1.00, 0.98 ROC area values of 1.00, 0.998 and precision of 99.9 and 98.3 % using 10-fold cross validation to predict the G-protein coupled receptors and their subfamilies respectively.


G-protein coupled receptors Weighted k-nearest neighbor Minimum redundancy maximum relevance Sequence derived properties Matthew’s correlation coefficient 


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Copyright information

© Springer India 2016

Authors and Affiliations

  • Arvind Kumar Tiwari
    • 1
  • Rajeev Srivastava
    • 1
  • Subodh Srivastava
    • 2
  • Shailendra Tiwari
    • 1
  1. 1.Department of Computer Science & EngineeringIndian Institute of Technology (BHU)VaranasiIndia
  2. 2.School of Biomedical EngineeringIndian Institute of Technology (BHU)VaranasiIndia

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