Classifying G-Protein Coupled Receptors with Hydropathy Blocks and Support Vector Machines

  • Xing-Ming Zhao
  • De-Shuang Huang
  • Shiwu Zhang
  • Yiu-ming Cheung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


This paper developes a new method for recognizing G-Protein Coupled Receptors (GPCRs) based on features generated from the hydropathy properties of the amino acid sequences. Using the hydropathy characteristics, namely hydropathy blocks, the protein sequences are converted into fixed-dimensional feature vectors. Subsequently, the Support Vector Machine (SVM) classifier is utilized to identify the GPCR proteins belonging to the same families or subfamilies. The experimental results on GPCR datasets show that the proteins belonging to the same family or subfamily can be identified using features generated based on the hydropathy blocks.


Support Vector Machine Protein Sequence Feature Vector Dipeptide Composition Amino Acid Alphabet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xing-Ming Zhao
    • 1
  • De-Shuang Huang
    • 1
  • Shiwu Zhang
    • 2
  • Yiu-ming Cheung
    • 3
  1. 1.Institute of Intelligent MachinesCASHefei, AnhuiChina
  2. 2.Department of Precision Machinery and InstrumentationUniversity of Science and Technology of ChinaHefei, AnhuiChina
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

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