Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network
Identifieur interne : 002739 ( Main/Exploration ); précédent : 002738; suivant : 002740Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network
Auteurs : Shaomin Yan [République populaire de Chine] ; Guang Wu [République populaire de Chine]Source :
- Annals of Biomedical Engineering [ 0090-6964 ] ; 2010-03-01.
English descriptors
- KwdEn :
Abstract
Abstract: Quantification of mutation capacity within a protein could be a way to model the mutation relationship not only because history might not leave many cues on the causes for mutations but also the evolved protein might no longer be subject to previous mutation causes. Randomness should play a constant role in engineering mutations in proteins because randomness suggests the maximal probability of occurrence by which a protein would be constructed with the least time and energy to meet the speed of rapidly changing environments. Since 1999, we have developed three approaches for quantifying of randomness of protein by which each amino acid has three numeric values. In this study, we model our three random numeric values in each amino acid with occurrence and non-occurrence of mutation, which are classified as unity and zero, using a 3-6-1 feedforward backpropagation neural network to predict the mutation positions in H5N1 neuraminidases. The results show that the neural network can capture the mutation relationship as measured by prediction sensitivity, specificity, and total correct rate. With the help of translation probability between RNA codes and mutated amino acids, we predict the would-be-mutated amino acids at predicted mutation positions.
Url:
DOI: 10.1007/s10439-010-9907-7
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 000C24
- to stream Istex, to step Curation: 000C24
- to stream Istex, to step Checkpoint: 000992
- to stream Main, to step Merge: 002778
- to stream Main, to step Curation: 002739
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network</title><author><name sortKey="Yan, Shaomin" sort="Yan, Shaomin" uniqKey="Yan S" first="Shaomin" last="Yan">Shaomin Yan</name></author><author><name sortKey="Wu, Guang" sort="Wu, Guang" uniqKey="Wu G" first="Guang" last="Wu">Guang Wu</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:D98CD132E69A675CAA8FBE208E32967F96654DAE</idno><date when="2010" year="2010">2010</date><idno type="doi">10.1007/s10439-010-9907-7</idno><idno type="url">https://api.istex.fr/ark:/67375/VQC-8240J2HV-R/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">000C24</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">000C24</idno><idno type="wicri:Area/Istex/Curation">000C24</idno><idno type="wicri:Area/Istex/Checkpoint">000992</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">000992</idno><idno type="wicri:doubleKey">0090-6964:2010:Yan S:prediction:of:mutation</idno><idno type="wicri:Area/Main/Merge">002778</idno><idno type="wicri:Area/Main/Curation">002739</idno><idno type="wicri:Area/Main/Exploration">002739</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network</title><author><name sortKey="Yan, Shaomin" sort="Yan, Shaomin" uniqKey="Yan S" first="Shaomin" last="Yan">Shaomin Yan</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, 98 Daling Road, 530007, Nanning, Guangxi</wicri:regionArea><wicri:noRegion>Guangxi</wicri:noRegion></affiliation></author><author><name sortKey="Wu, Guang" sort="Wu, Guang" uniqKey="Wu G" first="Guang" last="Wu">Guang Wu</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Computational Mutation Project, DreamSciTech Consulting, 301, Building 12, Nanyou A-zone, Jiannan Road, 518054, Shenzhen, Guangdong</wicri:regionArea><wicri:noRegion>Guangdong</wicri:noRegion></affiliation><affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Annals of Biomedical Engineering</title><title level="j" type="sub">The Journal of the Biomedical Engineering Society</title><title level="j" type="abbrev">Ann Biomed Eng</title><idno type="ISSN">0090-6964</idno><idno type="eISSN">1573-9686</idno><imprint><publisher>Springer US; http://www.springer-ny.com</publisher><pubPlace>Boston</pubPlace><date type="published" when="2010-03-01">2010-03-01</date><biblScope unit="volume">38</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="984">984</biblScope><biblScope unit="page" to="992">992</biblScope></imprint><idno type="ISSN">0090-6964</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0090-6964</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Amino acid</term><term>Modeling</term><term>Mutation</term><term>Neural network</term><term>Neuraminidase</term><term>Probability</term><term>Randomness</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Quantification of mutation capacity within a protein could be a way to model the mutation relationship not only because history might not leave many cues on the causes for mutations but also the evolved protein might no longer be subject to previous mutation causes. Randomness should play a constant role in engineering mutations in proteins because randomness suggests the maximal probability of occurrence by which a protein would be constructed with the least time and energy to meet the speed of rapidly changing environments. Since 1999, we have developed three approaches for quantifying of randomness of protein by which each amino acid has three numeric values. In this study, we model our three random numeric values in each amino acid with occurrence and non-occurrence of mutation, which are classified as unity and zero, using a 3-6-1 feedforward backpropagation neural network to predict the mutation positions in H5N1 neuraminidases. The results show that the neural network can capture the mutation relationship as measured by prediction sensitivity, specificity, and total correct rate. With the help of translation probability between RNA codes and mutated amino acids, we predict the would-be-mutated amino acids at predicted mutation positions.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Yan, Shaomin" sort="Yan, Shaomin" uniqKey="Yan S" first="Shaomin" last="Yan">Shaomin Yan</name></noRegion><name sortKey="Wu, Guang" sort="Wu, Guang" uniqKey="Wu G" first="Guang" last="Wu">Guang Wu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/SrasV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002739 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 002739 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= SrasV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:D98CD132E69A675CAA8FBE208E32967F96654DAE
|texte= Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network
}}
| This area was generated with Dilib version V0.6.33. |  |