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The Application of Support Vector Machine and Behavior Knowledge Space in the Disulfide Connectivity Prediction Problem

  • Hong-Yu Chen
  • Kuo-Tsung Tseng
  • Chang-Biau YangEmail author
  • Chiou-Yi Hor
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 454)

Abstract

In this paper, we apply support vector machine (SVM) and behavior knowledge space (BKS) to the disulfide connectivity prediction problem. The problem aims to establish the disulfide connectivity pattern of the target protein. It is an important problem since a disulfide bond, formed by two oxidized cysteines, plays an important role in the protein folding and structure stability. The disulfide connectivity prediction problem is difficult because the number of possible patterns grows rapidly with respect to the number of cysteines. We discover some rules to discriminate the patterns with high accuracy in various methods. Then, the pattern-wise and pair-wise BKS methods to fuse multiple classifiers constructed by the SVM methods are proposed. Finally, the CSP (cysteine separation profile) method is also applied to form our hybrid method. We perform some simulation experiments with the 4-fold cross-validation on SP39 dataset. The prediction accuracy of our method is increased to 69.1 %, which is better than the best previous result 65.9 %.

Keywords

Disulfide bond Cysteine Connectivity pattern Support vector machine Behavior knowledge space 

Notes

Acknowledgements

This research work was partially supported by the National Science Council of Taiwan under contract NSC 100-2221-E-242-003.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Hong-Yu Chen
    • 1
  • Kuo-Tsung Tseng
    • 2
  • Chang-Biau Yang
    • 1
    Email author
  • Chiou-Yi Hor
    • 1
  1. 1.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan
  2. 2.Department of Shipping and Transportation ManagementNational Kaohsiung Marine UniversityKaohsiungTaiwan

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