Using Decision Templates to Predict Subcellular Localization of Protein

  • Jianyu Shi
  • Shaowu Zhang
  • Quan Pan
  • Yanning Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)


Theoretical and computational methods for the prediction of protein subcellular localization have been proposed and are developing continuously. Many representations of protein sequence are proposed but a new problem arises: how to organize them together to improve prediction. It is an available solution to serialize multiple representations to single bigger one, but is still hard to avoid calculation error derived from greatly different feature values and causes huge computational burden natively because of high dimensional feature vector. We present a novel method based on decision templates(DT) for such problems in this paper. First, a protein sequence is represented as three new types of feature vectors. Then, the feature vectors are further taken as the inputs of individual SVM classifiers respectively. Finally, the outputs of these classifiers are aggregated by decision templates. The results demonstrate that DT is superior to other methods of subcellular localization prediction.


decision templates subcellular localization prediction multi-scale energy moment descriptor amino acid composition distribution support vector machines 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jianyu Shi
    • 1
  • Shaowu Zhang
    • 2
  • Quan Pan
    • 2
  • Yanning Zhang
    • 1
  1. 1.School of Computer Science and Engineering 
  2. 2.School of Automation, Northwestern Polytechnical University, 710072 Xi anChina

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