Classifying protein sequences using hydropathy blocks

  • Authors:
  • De-Shuang Huang;Xing-Ming Zhao;Guang-Bin Huang;Yiu-Ming Cheung

  • Affiliations:
  • Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China and Department of Automation, University of Science and Technol ...;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

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Abstract

The annotation of proteins can be achieved by classifying the protein of interest into a certain known protein family to induce its functional and structural features. This paper presents a new method for classifying protein sequences based upon the hydropathy blocks occurring in protein sequences. First, a fixed-dimensional feature vector is generated for each protein sequence using the frequency of the hydropathy blocks occurring in the sequence. Then, the support vector machine (SVM) classifier is utilized to classify the protein sequences into the known protein families. The experimental results have shown that the proteins belonging to the same family or subfamily can be identified using features generated from the hydropathy blocks.