Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model
H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and fl...
Saved in:
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/155627 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-155627 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1556272022-09-26T01:57:10Z Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model Yin, Rui Tran, Viet Hung Zhou, Xinrui Zheng, Jie Kwoh, Chee Keong School of Computer Science and Engineering Genome Institute of Singapore, A*STAR Engineering::Computer science and engineering Influenza H1N1 Antigenic Variant Pandemics Epidemics Stacking Model H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain's antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance. Ministry of Education (MOE) Published version This study is supported by AcRF Tier 2 grant MOE2014-T2-2-023, Ministry of Education, Singapore. 2022-03-14T02:07:22Z 2022-03-14T02:07:22Z 2018 Journal Article Yin, R., Tran, V. H., Zhou, X., Zheng, J. & Kwoh, C. K. (2018). Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model. PLOS ONE, 13(12), e0207777-. https://dx.doi.org/10.1371/journal.pone.0207777 1932-6203 https://hdl.handle.net/10356/155627 10.1371/journal.pone.0207777 30576319 2-s2.0-85058915733 12 13 e0207777 en MOE2014-T2-2-023 PLOS ONE 10.21979/N9/O5XL2X © 2018 Yin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering Influenza H1N1 Antigenic Variant Pandemics Epidemics Stacking Model |
spellingShingle |
Engineering::Computer science and engineering Influenza H1N1 Antigenic Variant Pandemics Epidemics Stacking Model Yin, Rui Tran, Viet Hung Zhou, Xinrui Zheng, Jie Kwoh, Chee Keong Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model |
description |
H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain's antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance. |
author2 |
School of Computer Science and Engineering |
author_facet |
School of Computer Science and Engineering Yin, Rui Tran, Viet Hung Zhou, Xinrui Zheng, Jie Kwoh, Chee Keong |
format |
Article |
author |
Yin, Rui Tran, Viet Hung Zhou, Xinrui Zheng, Jie Kwoh, Chee Keong |
author_sort |
Yin, Rui |
title |
Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model |
title_short |
Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model |
title_full |
Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model |
title_fullStr |
Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model |
title_full_unstemmed |
Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model |
title_sort |
predicting antigenic variants of h1n1 influenza virus based on epidemics and pandemics using a stacking model |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/155627 |
_version_ |
1745574610265440256 |