Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

ABSTRACT: Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization,

SUBMITTER: Heinson AI 

PROVIDER: S-EPMC5343848 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature