1. Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus
- Author
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Tong Zhang, Lei Zhong, Feng Wen, Xiu-Feng Wan, Lei Han, and Lei Li
- Subjects
Statistics and Probability ,Computer science ,Hemagglutinin (influenza) ,Hemagglutinin Glycoproteins, Influenza Virus ,Computational biology ,medicine.disease_cause ,Biochemistry ,Virus ,Serology ,Sparse learning ,03 medical and health sciences ,Vaccine strain ,Antigen ,Influenza, Human ,Influenza A virus ,medicine ,Antigenic variation ,Humans ,Antigens, Viral ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,Mutation ,biology ,Influenza A Virus, H3N2 Subtype ,030302 biochemistry & molecular biology ,Computational Biology ,Influenza a ,Antigenic Variation ,Original Papers ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,Influenza Vaccines ,biology.protein ,Graph (abstract data type) ,Software - 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 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 and implementation MATLAB source codes developed for GG-MTSL are available through http://sysbio.cvm.msstate.edu/files/GG-MTSL/. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2018