Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.

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 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 (eXtr

SUBMITTER: Ong E 

PROVIDER: S-EPMC7214037 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature