SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins.

Journal: International journal of molecular sciences
Published Date: Jun 15, 2018

Abstract

Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.

Authors

  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Guangmin Liang
    School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China. gmliang@szpt.edu.cn.
  • Shuhua Shi
    School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China. sshua@szpt.edu.cn.
  • Changrui Liao
    Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things, Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.

Keywords

External Resources

Popular Topics
Recent Journals