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Identification of hormone binding proteins based on machine learning methods

1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
2 National Research Institute for Family Planning, Beijing 100081, China
3 National Center of Human Genetic Resources, Beijing 100081, China
4 Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
5 Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China

Special Issues: Machine Learning in Molecular Biology

The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequisite for understanding its biological functions and molecular mechanisms. Since experimental methods are still labor intensive and cost ineffective to identify HBP, it’s necessary to develop computational methods to accurately and efficiently identify HBP. In this paper, a machine learning-based method was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method. In the 5-fold cross-validation test, the proposed method yielded an overall accuracy of 97.15%. For the convenience of scientific community, a user-friendly webserver called HBPred2.0 was built, which could be freely accessed at http://lin-group.cn/server/HBPred2.0/.
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Keywords hormone binding protein; tripeptide composition; binomial distribution method; feature selection; support vector machine; webserver

Citation: Jiu-Xin Tan, Shi-Hao Li, Zi-Mei Zhang, Cui-Xia Chen, Wei Chen, Hua Tang, Hao Lin. Identification of hormone binding proteins based on machine learning methods. Mathematical Biosciences and Engineering, 2019, 16(4): 2466-2480. doi: 10.3934/mbe.2019123


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