Protein Subcellular Location Prediction Based on Pseudo Amino Acid Composition and Immune Genetic Algorithm

  • Tongliang Zhang
  • Yongsheng Ding
  • Shihuang Shao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


Protein subcellular location prediction with computational method is still a hot spot in bioinformatics. In this paper, we present a new method to predict protein subcellular location, which based on pseudo amino acid composition and immune genetic algorithm. Hydrophobic patterns of amino acid couples and approximate entropy are introduced to construct pseudo amino acid composition. Immune Genetic algorithm (IGA) is applied to find the fittest weight factors for pseudo amino acid composition, which are crucial in this method. As such, high success rates are obtained by both self-consistency test and jackknife test. More than 80% predictive accuracy is achieved in independent dataset test. The result demonstrates that this new method is practical. And, the method illuminates that the hydrophobic patterns of protein sequence influence its subcellular location.


Support Vector Machine Markov Chain Model Simple Genetic Algorithm Approximate Entropy Protein Subcellular Location 
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

  • Tongliang Zhang
    • 1
  • Yongsheng Ding
    • 1
  • Shihuang Shao
    • 1
  1. 1.College of Information Sciences and TechnologyDonghua UniversityShanghaiChina

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