Antigenic: An improved prediction model of protective antigens

作者：

Highlights：

• We present a new computational model to identify protective antigens in an efficient and accurate way.

• We extract meaningful information directly from the protein sequences, without any dependence on functional domain or structural information.

• Named as antigenic, our proposed model demonstrates superior performance compared to the state-of-the-art predictors on a benchmark dataset.

• A publicly accessible web interface has been established at: http://77.68.43.135:8080/Antigenic/.

摘要

•We present a new computational model to identify protective antigens in an efficient and accurate way.•We extract meaningful information directly from the protein sequences, without any dependence on functional domain or structural information.•Named as antigenic, our proposed model demonstrates superior performance compared to the state-of-the-art predictors on a benchmark dataset.•A publicly accessible web interface has been established at: http://77.68.43.135:8080/Antigenic/.

论文关键词：Antigens,Non-antigens,Vaccine,Reverse vaccinology,Classification,Prediction,Support vector machine,Random forest,PseAAC

论文评审过程：Received 4 May 2018, Revised 31 October 2018, Accepted 28 December 2018, Available online 3 January 2019, Version of Record 17 January 2019.

论文官网地址：https://doi.org/10.1016/j.artmed.2018.12.010