Amino Acids

, Volume 38, Issue 4, pp 975–983 | Cite as

Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection

  • Quan Gu
  • Yong-Sheng Ding
  • Xiao-Ying Jiang
  • Tong-Liang Zhang
Original Article


Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.


Apoptosis protein subcellular location Feature selection Ensemble classifier Fuzzy K-nearest neighbor classifier 



The authors wish to thank Dr. Z. H. Zhang for providing the datasets. This work was supported in part by Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China (No. 20060255006), Project of the Shanghai Committee of Science and Technology (No. 08JC1400100), Shanghai Talent Developing Foundation (No. 001), Specialized Foundation for Excellent Talent from Shanghai, and the Open Fund from the Key Laboratory of MICCAI of Shanghai (06dz22013).


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Quan Gu
    • 1
  • Yong-Sheng Ding
    • 1
    • 2
  • Xiao-Ying Jiang
    • 3
  • Tong-Liang Zhang
    • 4
  1. 1.College of Information Sciences and TechnologyDonghua UniversityShanghaiChina
  2. 2.Engineering Research Center of Digitized Textile & Fashion TechnologyMinistry of EducationShanghaiChina
  3. 3.School of Chemistry and Chemical EngineeringHenan Institute of Science and TechnologyHenanChina
  4. 4.Research Institute of HighwayResearch Institute of Highway Ministry of CommunicationsBeijingChina

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