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    Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine.

    • Authors:
    • Bo Liao
      College of Information Science and Engineering
      China
      Xianhong Li
      School of Nursing
      China
      Huimin Xu
      Virginia Polytechnic Institute and State University
      United States
      Jing Chen
      University of Kentucky
      Lexington | United States
      Zhuoxing Shi
      Zhejiang Sci-Tech University
      China
      Qi Dai
      Vanderbilt University School of Medicine
      United States
      Yuhua Yao
      Zhejiang Sci-Tech University
      China
    Artif Intell Med 2017 05 24;78:41-46. Epub 2017 May 24.
    College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China; School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China. Electronic address:
    Objectives: In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins.

    Methods: In the proposed methodology, the novel evolutionary-conservative information is introduced to represent protein sequences. Meanwhile, based on the proportion of golden section in mathematics, position-specific scoring matrix (PSSM) is divided into several blocks. Then, these features are predicted by support vector machine (SVM) and the predictive capability of proposed method is implemented by jackknife test RESULTS: The results show that the golden section method is better than no segmentation method. The overall accuracy for ZD98 and CL317 is 98.98% and 91.11%, respectively, which indicates that our method can play a complimentary role to the existing methods in the relevant areas.

    Conclusions: The proposed feature representation is powerful and the prediction accuracy will be improved greatly, which denotes our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.
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    http://dx.doi.org/10.1016/j.artmed.2017.05.007DOI ListingPossible

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