Phenotypic, functional and taxonomic features predict host-pathogen interactions: Table S1; Figure S1

Abstract

Identification 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 species of emerging pathogens. Matching the phenotypes elicited by pathogens with phenotypes associated with host proteins might improve the prediction of HPIs. We developed an ontology-based method that prioritizes potential interaction protein partners for pathogens using machine learning models. Our method exploits the underlying disease mechanisms by associating phenotypic and functional features of pathogens and human proteins, corroborated by multiple ontologies as background knowledge. Additionally, by embedding the phenotypic information of the pathogens within a formally represented taxonomy, we demonstrate that our model can also 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.