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Bioinformatics. 2020 Feb 25. pii: btaa119. doi: 10.1093/bioinformatics/btaa119. [Epub ahead of print]

Vaxign-ML: Supervised Machine Learning Reverse Vaccinology Model for Improved Prediction of Bacterial Protective Antigens.

Author information

1
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
2
Department of Pathogenobiology, Daqing Branch of Harbin Medical University, Daqing, China.
3
Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MIs, USA.
4
College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA.
5
Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.
6
Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.

Abstract

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 the challenges in prediction accuracy and program accessibility.

RESULTS:

This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens. 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 five-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, Vaxign-ML, was compared to three publicly available RV programs with a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting bacterial protective antigens. Vaxign-ML is deployed in a publicly available web server.

AVAILABILITY:

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.

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