Author(s)

Yajie Cao¹, Jinglin Liu^1*, Tao Liu^2,3, Dejiang Liu^4*, Yunfei Wu¹

¹College of Science, Jiamusi University, Jiamusi, China.
²College of Science, Hebei Polytechnic University, Tangshan, China.
³Department of Computer Science, University of Georgia, Georgia, USA.
⁴College of Science, Northeast Forestry University, Harbin, China.

In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to predict the continuous B-cell epitopes, and finally the predictive model for the B-cells epitopes was established. Comparing with the other predictive models, the prediction performance of this model is more excellent (AUC = 0.723). For the purpose of verifying the performance of the model, the prediction to the SWISS PROT NUMBER: P08677 was carried on, and the satisfying results were obtained.

Continuous B-Cell Epitopes, BP Neural Network, Theory Method, Predictive Model

Cao, Y. , Liu, J. , Liu, T. , Liu, D. and Wu, Y. (2016) Theoretical Study of Continuous B-Cell Epitopes with Developed BP Neural Network. Computational Chemistry, 4, 83-90. doi: 10.4236/cc.2016.43008.