Classifying protein sequences using hydropathy blocks

De Shuang Huang*, Xing Ming Zhao, Guang Bin Huang, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

69 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2293-2300
Number of pages8
JournalPattern Recognition
Issue number12
Publication statusPublished - Dec 2006

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Hydropathy blocks
  • Protein sequence classification
  • Support vector machine


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