Yuqing Qian1, Limin Jiang2, Yijie Ding3, Jijun Tang2, Fei Guo4. 1. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, People's Republic of China. 2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, People's Republic of China. 3. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, People's Republic of China. wuxi_dyj@163.com. 4. School of Computer Science and Engineering, Central South University, Changsha, People's Republic of China. guofeieileen@163.com.
Abstract
BACKGROUND: DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. RESULTS: In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([Formula: see text]) and PDB186 ([Formula: see text]), respectively. CONCLUSION: Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.
BACKGROUND: DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. RESULTS: In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([Formula: see text]) and PDB186 ([Formula: see text]), respectively. CONCLUSION: Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.
Authors: S F Altschul; T L Madden; A A Schäffer; J Zhang; Z Zhang; W Miller; D J Lipman Journal: Nucleic Acids Res Date: 1997-09-01 Impact factor: 16.971