Yijie Ding, Feng Chen, Xiaoyi Guo*, Jijun Tang and Hongjie Wu* Pages 1 - 9 ( 9 )
Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive.
Objective: In past several years, various computational methods have been developed to detect DNA-binding proteins. However, most of them do not integrate multiple information.
Method: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, we extract several feature and construct multiple kernels. Then, multiple kernels are linear combined by Multiple Kernel Learning (MKL). At last, a final SVM model, constructed by combined kernel, is built to predict DNA-binding proteins.
Results: The proposed method is tested on two benchmark data sets. Compared with other existing method, our approach is comparable, even better than other methods on some data sets.
Conclusion: We can conclude that MK-SVM is more suitable than common SVM, as the classifier for DNA-binding proteins identification.
DNA-binding proteins, feature extraction, support vector machine, multiple kernel learning, kernel alignment, crystallography
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, P.R., School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, P.R., Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, P.R., Department of Computer Science and Engineering, University of South Carolina, Columbia, School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, P.R.