ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.234
A Prediction and Correction Model for Protein Secondary Structure Prediction
Abstract— Protein secondary structure prediction is one of the central topics in bioinformatics. Machine
learning techniques have been widely applied to solve the problem, and many methods have gained
substantial success in this research area. In this paper, we propose a prediction and correction model to
improve the performance of secondary structure prediction. This model has a correction process on the basis
of classification (SVM). Statistical analysis was carried out on the prediction results. These statistical results
is used as a priori knowledge to find error patterns and design correction methods to correct it. The
experimental results show that our proposed model can indeed improve the prediction accuracy.
Index Terms— Protein secondary structure, support vector machine, Random Subspace.
Yuming Ma, Jinyong Cheng, Yihui Liu
School of Information Science, Qilu University of Technology, CHINA
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.17Xsrc="http://www.wcse.org/uploadfile/2019/0823/20190823055609629.png" style="width: 120px; height: 68px;" />[Download]
Cite: Yuming Ma, Jinyong Cheng, Yihui Liu, "A Prediction and Correction Model for Protein Secondary Structure Prediction," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 1353-1357, Beijing, 25-27 June, 2017.