The project developed a Graph-Guided Multi-Task Sparse Learning (GG-MTSL) model that uses multisourced serologic data to learn antigenicity-associated mutations and infer antigenic variants. By applying GG-MTSL to influenza H3N2 hemagglutinin sequences, we showed the method enables rapid characterization of antigenic profiles and identification of antigenic variants in real time and on a large scale. Furthermore, sequences can be generated directly by using clinical samples, thus minimizing biases due to culture-adapted mutation during virus isolation.
Let me know if you have any questions or comments at [email protected]