Modeling Human Innate Immune Response Using Graph Neural Networks

Since the rapid outbreak of Covid-19, profound research interest has emerged to understand the innate immune response to viruses to enable appropriate vaccination. This understanding can help to inhibit virus replication, prolong adaptive immune response, accelerated virus clearance, and tissue recovery, a key milestone to combat coronaviruses (CoVs), e.g., Covid-19. An innate immune system triggers inflammatory responses against CoVs upon recognition of viruses. An appropriate defense against various coronavirus strains requires a deep understanding of the innate immune response system. Current deep learning approaches focus more on Covid-19 detection and pay no attention to understand the immune response once a virus invades. In this work, we propose a graph neural network-based (GNN) model that exploits the interactions between pattern recognition receptors (PRRs)to understand the human immune response system. PRRs are germline-encoded proteins that identify molecules related to pathogens and initiate a defense mechanism against the related pathogens, thereby aiding the innate immune response system. An understanding of PRR interactions can help to recognize pathogen-associated molecular patterns (PAMPs) to predict the activation requirements of each PRR. The immune response information of each PRR is derived from combining its historical PAMPs activation coupled with the modeled effect on the same from PRRs in its neighborhood. On one hand, this work can help to understand how long Covid-19 can confer immunity for a strong immune response. On the other hand, this GNN-based understanding can also abode well for appropriate vaccine development efforts against CoVs. Our proposal has been evaluated using CoVs immune response dataset, with results showing an average IFNs activation prediction accuracy of 90%, compared to 85% using feed-forward neural networks.