Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus.

ABSTRACT:

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 t

SUBMITTER: Han L 

PROVIDER: S-EPMC6298058 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature