Graph-Guided Multi-Task Sparse Learning Model: a Method for Identifying Antigenic Variants of Influenza A(H3N2) Virus.

Motivation: Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly, and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies, and protocol implementation. A novel method without such limitations is needed for antigenic variant identification.

Results: We developed a Graph-Guided Multi-Task Sparse Learning (GG-MTSL) model that uses multi-sourced 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.

Availability: MATLAB source codes developed for GG-MTSL are available through https://sysbio.cvm.msstate.edu/files/GG-MTSL/.

Contact: [email protected].

Supplementary information: Supplementary data are available at Bioinformatics online.