H2N2V1, PubMed, Corpus, bibRecord, 000093

Using multiple linear regression and physicochemical changes of amino acid mutations to predict antigenic variants of influenza A/H3N2 viruses.

Identifieur interne : 000093 ( PubMed/Corpus ); précédent : 000092; suivant : 000094

Using multiple linear regression and physicochemical changes of amino acid mutations to predict antigenic variants of influenza A/H3N2 viruses.

Auteurs : Haibo Cui ; Xiaomei Wei ; Yu Huang ; Bin Hu ; Yaping Fang ; Jia Wang

Source :

Bio-medical materials and engineering [ 1878-3619 ] ; 2014.

RBID : pubmed:25227088

English descriptors

KwdEn :
- Amino Acid Sequence, Antigens, Viral (chemistry), Antigens, Viral (genetics), Base Sequence, Computer Simulation, DNA Mutational Analysis (methods), Data Interpretation, Statistical, Influenza A Virus, H2N2 Subtype (genetics), Linear Models, Molecular Sequence Data, Regression Analysis.
MESH :
- chemical , chemistry : Antigens, Viral.
- chemical , genetics : Antigens, Viral.
- genetics : Influenza A Virus, H2N2 Subtype.
- methods : DNA Mutational Analysis.
- Amino Acid Sequence, Base Sequence, Computer Simulation, Data Interpretation, Statistical, Linear Models, Molecular Sequence Data, Regression Analysis.

Abstract

Among human influenza viruses, strain A/H3N2 accounts for over a quarter of a million deaths annually. Antigenic variants of these viruses often render current vaccinations ineffective and lead to repeated infections. In this study, a computational model was developed to predict antigenic variants of the A/H3N2 strain. First, 18 critical antigenic amino acids in the hemagglutinin (HA) protein were recognized using a scoring method combining phi (ϕ) coefficient and information entropy. Next, a prediction model was developed by integrating multiple linear regression method with eight types of physicochemical changes in critical amino acid positions. When compared to other three known models, our prediction model achieved the best performance not only on the training dataset but also on the commonly-used testing dataset composed of 31878 antigenic relationships of the H3N2 influenza virus.

DOI: 10.3233/BME-141201
PubMed: 25227088

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pubmed:25227088

Le document en format XML

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<affiliation><nlm:affiliation>School of Computer Science and Information Engineering, Hubei University, Wuhan 430070, China.</nlm:affiliation>
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<author><name sortKey="Wei, Xiaomei" sort="Wei, Xiaomei" uniqKey="Wei X" first="Xiaomei" last="Wei">Xiaomei Wei</name>
<affiliation><nlm:affiliation>College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.</nlm:affiliation>
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<author><name sortKey="Huang, Yu" sort="Huang, Yu" uniqKey="Huang Y" first="Yu" last="Huang">Yu Huang</name>
<affiliation><nlm:affiliation>College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.</nlm:affiliation>
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<author><name sortKey="Hu, Bin" sort="Hu, Bin" uniqKey="Hu B" first="Bin" last="Hu">Bin Hu</name>
<affiliation><nlm:affiliation>College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.</nlm:affiliation>
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<author><name sortKey="Fang, Yaping" sort="Fang, Yaping" uniqKey="Fang Y" first="Yaping" last="Fang">Yaping Fang</name>
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<author><name sortKey="Wang, Jia" sort="Wang, Jia" uniqKey="Wang J" first="Jia" last="Wang">Jia Wang</name>
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<term>Computer Simulation</term>
<term>DNA Mutational Analysis (methods)</term>
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<term>Base Sequence</term>
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<front><div type="abstract" xml:lang="en">Among human influenza viruses, strain A/H3N2 accounts for over a quarter of a million deaths annually. Antigenic variants of these viruses often render current vaccinations ineffective and lead to repeated infections. In this study, a computational model was developed to predict antigenic variants of the A/H3N2 strain. First, 18 critical antigenic amino acids in the hemagglutinin (HA) protein were recognized using a scoring method combining phi (ϕ) coefficient and information entropy. Next, a prediction model was developed by integrating multiple linear regression method with eight types of physicochemical changes in critical amino acid positions. When compared to other three known models, our prediction model achieved the best performance not only on the training dataset but also on the commonly-used testing dataset composed of 31878 antigenic relationships of the H3N2 influenza virus. </div>
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Using multiple linear regression and physicochemical changes of amino acid mutations to predict antigenic variants of influenza A/H3N2 viruses.

Using multiple linear regression and physicochemical changes of amino acid mutations to predict antigenic variants of influenza A/H3N2 viruses.

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