Vaccine development through graph-based machine learning generated host-pathogen interactome

Dong Xia (primary)
Comparative Biomedical Sciences
Royal Veterinary College
Laura Toni (secondary)
Institute of Communications and Connected Systems


Protein-protein interactions (PPIs) underlie most cellular functions, where pathogens interact with hubs and bottlenecks of the host PPI network. Modelling proteins as graphs allows us to study PPI as phenomena on irregular but structure geometry. Graph-based machine learning allows inference of structure and exploitation for PPI prediction. This project will generate a host-pathogen interactome map using graph-based machine learning for coccidiosis, a disease caused by Eimeria with annual losses exceeding €2 billion. Current vaccines are sub-optimal and new subunit vaccines are required. The model generated will significantly advance our ability to identify vaccine targets and utilize host-networks to optimize responses.


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Animal disease, health and welfare
Area of Biology
BiotechnologyCell Biology
Techniques & Approaches
BioinformaticsMathematics / StatisticsMolecular BiologySimulation / Modelling