Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity.

ABSTRACT: The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, vi

SUBMITTER: Wang P 

PROVIDER: S-EPMC6206390 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature