Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses

Huang, Li; Li, Xianhong; Guo, Ping; Yao, Yao; Liu, Bo; Zhang, Weiwei; Wang, Fayou; Yang, Jiasheng; Zhao, Yulong; Sun, Hailiang; He, Ping‐an; Yang, Jialiang

doi:10.1093/bioinformatics/btx390

Cited by 33 publications

(29 citation statements)

References 22 publications

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“…As can be seen from the table, the lowest RMSE 0.5982 is achieved at window size 32 and rank 9 (mixed rank of 8 and 9). According to Huang et al ( 2017 ), the best RMSE value for BMCSI is 0.6586 and those for antigenic cartography (Smith et al, 2004 ; Cai et al, 2010 ) and AntigenMap (Barnett et al, 2012 ) are 1.04 and 1.05, respectively. The above results indicate that the complete HA1 protein sequence information with discriminating antigenic determinant regions is a good compensation for low rank matrix completion.…”

Section: Resultsmentioning

confidence: 99%

“…The HI table contains Type I data, Type II data, Type III data, which are regular HI titers, low reactors (i.e., the HI titers less than a threshold) and missing values (Cai et al, 2010 ). Similar to many literatures (Smith et al, 2004 ; Cai et al, 2010 ; Sun et al, 2013 ; Huang et al, 2017 ), the HI table was normalized to facilitate subsequent analyses. We also downloaded HA protein sequences of viruses and vaccine strains related to HI tables from the NCBI influenza database.…”

Section: Methodsmentioning

confidence: 99%

“…In the literature (Huang et al, 2017 ), it has been shown that incorporating the HA protein sequence information of viruses into the matrix completion method achieves better results. However, since the model does not use vaccine strains HA protein sequence information, the use of information is incomplete.…”

Section: Methodsmentioning

confidence: 99%

“…The most effective way to prevent influenza virus infection is to inoculate vaccines with similar antigenicity to the influenza virus (Sun et al, 2013 ). Therefore, timely and accurate identification of the effectiveness of existing vaccines on circulating virus strains is critical for vaccine design and influenza surveillance (Smith et al, 2004 ; Huang et al, 2017 ). However, the task is challenging (Blackburne et al, 2008 ; Yao et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Imputation-based methods can better detect the antigenic evolutionary trend of H3N2 influenza virus, but it is still insufficient. For example, the accuracy of its predicted antigenic distance is yet to be improved (Huang et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity

et al. 2018

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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, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies.

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