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Open AccessArticle

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou’s Five-Step Rule

by 1,2, Yijie Ding 3,*, 4, 5 and Li Peng 1,2,*
1
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
2
Engineering Research Center of Internet of Things Applied Technology, Ministry of Education, Wuxi 214122, China
3
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
4
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
5
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(17), 4175; https://doi.org/10.3390/ijms20174175
Received: 30 July 2019 / Revised: 10 August 2019 / Accepted: 19 August 2019 / Published: 26 August 2019
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively. View Full-Text
Keywords: DNA-binding proteins prediction; fuzzy kernel ridge regression; multiple kernel learning; feature extraction; protein sequence DNA-binding proteins prediction; fuzzy kernel ridge regression; multiple kernel learning; feature extraction; protein sequence
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Zou, Y.; Ding, Y.; Tang, J.; Guo, F.; Peng, L. FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou’s Five-Step Rule. Int. J. Mol. Sci. 2019, 20, 4175.

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