Prediction of Protein Subcellular Localizations Using Moment Descriptors and Support Vector Machine

  • Jianyu Shi
  • Shaowu Zhang
  • Yan Liang
  • Quan Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)


As more and more genomes have been discovered in recent years, it is an urgent need to develop a reliable method to predict protein subcellular localization for further function exploration. However many well-known prediction methods based on amino acid composition, have no ability to utilize the information of sequence-order. Here we propose a novel method, named moment descriptor (MD), which can obtain sequence order information in protein sequence without the need of the information of physicochemical properties of amino acids. The presented method first constructs three types of moment descriptors, and then applies multi-class SVM to the Chou’s dataset. Through resubstitution, jackknife and independent tests, it is shown that the MD is better than other methods based on various types of extensions of amino acid compositions. Moreover, three multi-class SVMs show similar performance except for the training time.


Support Vector Machine Amino Acid Composition Directed Acyclic Graph Feature Extraction Method Protein Subcellular Localization 
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

  • Jianyu Shi
    • 1
  • Shaowu Zhang
    • 1
  • Yan Liang
    • 1
  • Quan Pan
    • 1
  1. 1.College of AutomationNorthwestern Polytechnical UniversityXi’anChina

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