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 %.
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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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Chen, HY., Tseng, KT., Yang, CB., Hor, CY. (2015). The Application of Support Vector Machine and Behavior Knowledge Space in the Disulfide Connectivity Prediction Problem. In: Fred, A., Dietz, J., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2013. Communications in Computer and Information Science, vol 454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46549-3_5
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