Taxonomic propagation of phenotypic features predict host pathogen interactions

Abstract

MotivationIdentification of host-pathogen interactions (HPIs) can reveal mechanistic insights of infectious diseases for potential treatments and drug discoveries. Current computational methods for the prediction of HPIs often rely on our knowledge on the sequences and functions of pathogen proteins, which is limited for many species, especially for emerging pathogens. Matching the phenotypes elicited by pathogens with phenotypes associated with host proteins might improve the prediction of HPIs. ResultsWe developed an ontology-based machine learning method that predicts potential interaction protein partners for pathogens. Our method exploits information about disease mechanisms through features learned from phenotypic, functional and taxonomic knowledge about pathogens and human proteins. Additionally, by embedding the phenotypic information of the pathogens within a formal representation of pathogen taxonomy, we demonstrate that our model can accurately predict interaction partners for pathogens without known phenotypes, using a combination of their taxonomic relationships with other pathogens and information from ontologies as background knowledge. Our results show that the integration of phenotypic, functional and taxonomic knowledge not only improves the prediction of HPIs, but also enables us to investigate novel pathogens in emerging infectious diseases. Availabilityhttps://github.com/bio-ontology-research-group/hpi-predict Contactrobert.hoehndorf@kaust.edu.sa