CWLy-SVM: A support vector machine-based tool for identifying cell wall lytic enzymes
Institution:
1. College of Intelligence and Computing, Tianjin University, Tianjin, China;2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China;3. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China;4. College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
Abstract:
Cell wall lytic enzymes, as an important biotechnical tool in drug development, agriculture and the food industry, have attracted more research attention. In this research, the accurate identification of cell wall lytic enzymes is one of the key and fundamental tasks. In this study, in order to eliminate the inefficiency of in vitro experiments, a support vector machine-based cell wall lytic enzyme identification model was constructed using bioinformatics. This machine learning process includes feature extraction, feature selection, model training and optimization. According to the jackknife cross validation test, this model obtained a sensitivity of 0.853, a specificity of 0.977, an MCC of 0.845 and an AUC of 0.915. These benchmark results demonstrate that the proposed model outperforms the state-of-the-art method and that it has powerful cell wall lytic enzyme identification ability. Furthermore, we comprehensively analyzed the selected optimal features and used the proposed model to construct a user friendly web server called the CWLy-SVM to identify cell wall lytic enzymes, which is available at http://server.malab.cn/CWLy-SVM/index.jsp.
