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 DNAbinding
proteins. However, most of them do not integrate multiple information.
Methods:
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.
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