Vaxign-ML：用于提高细菌保护性抗原预测准确性的监督机器学习反向疫苗学模型。
Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.
机构信息
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Department of Pathogenobiology, Daqing Branch of Harbin Medical University, Daqing 163319, China.
出版信息
Bioinformatics. 2020 May 1;36(10):3185-3191. doi: 10.1093/bioinformatics/btaa119.
MOTIVATION
Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility.
RESULTS
This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available.
AVAILABILITY AND IMPLEMENTATION
Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
反向疫苗学 (RV) 是理性疫苗设计的一个里程碑，机器学习 (ML) 已被应用于提高 RV 预测的准确性。然而，基于 ML 的 RV 在预测准确性和程序可访问性方面仍然面临挑战。
结果
本研究提出了 Vaxign-ML，这是一种用于预测细菌保护性抗原 (BPAgs) 的有监督 ML 分类器。为了确定具有优化条件的最佳 ML 方法，我们使用从定义明确的训练数据中提取的生物学和物理化学特征测试了五种 ML 方法。使用嵌套 5 折交叉验证和留一病原体验证来确保无偏性能评估和对新出现的病原体预测疫苗候选物的能力。表现最佳的模型（极端梯度增强）与三种公开可用的程序（Vaxign、VaxiJen 和 Antigenic）、一种基于 SVM 的方法和一种基于表位的方法进行了比较，使用了高质量的基准数据集。Vaxign-ML 在预测 BPAgs 方面表现出优异的性能。Vaxign-ML 托管在一个可公开访问的网络服务器上，也提供独立版本。
可用性和实现
Vaxign-ML 网站位于 http://www.violinet.org/vaxign/vaxign-ml，可在 https://hub.docker.com/r/e4ong1031/vaxign-ml 处获得独立的 Docker Vaxign-ML，源代码可在 https://github.com/VIOLINet/Vaxign-ML-docker 处获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
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