MersV1, PubMed, Corpus, bibRecord, 001E08

Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.

Identifieur interne : 001E08 ( PubMed/Corpus ); précédent : 001E07; suivant : 001E09

Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.

Auteurs : Yu-Fang Qin ; Chun-Hua Wang ; Xiao-Qing Yu ; Jie Zhu ; Tai-Gang Liu ; Xiao-Qi Zheng

Source :

Protein and peptide letters [ 1875-5305 ] ; 2012.

RBID : pubmed:22316305

English descriptors

KwdEn :
- Algorithms, Amino Acids (chemistry), Computational Biology, Databases, Protein, Neural Networks, Computer, Protein Folding, Protein Structure, Tertiary, Proteins (chemistry), Proteins (classification), Sequence Analysis, Protein, Support Vector Machine.
MESH :
- chemical , chemistry : Amino Acids, Proteins.
- chemical , classification : Proteins.
- Algorithms, Computational Biology, Databases, Protein, Neural Networks, Computer, Protein Folding, Protein Structure, Tertiary, Sequence Analysis, Protein, Support Vector Machine.

Abstract

Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. In this paper, a new feature extraction approach based on relative polypeptide composition is introduced. This approach could take into account the background distribution of a given k-mer under a Markov model of order k-2, and avoid the curse of dimensionality with the increase of k by using a T-statistic feature selection strategy. The selected features are then fed to a support vector machine to perform the prediction. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction.

DOI: 10.2174/092986612799789350
PubMed: 22316305

Links to Exploration step

pubmed:22316305

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.</title>
<author><name sortKey="Qin, Yu Fang" sort="Qin, Yu Fang" uniqKey="Qin Y" first="Yu-Fang" last="Qin">Yu-Fang Qin</name>
<affiliation><nlm:affiliation>Department of Mathematics, Shanghai Normal University, 200234 Shanghai, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wang, Chun Hua" sort="Wang, Chun Hua" uniqKey="Wang C" first="Chun-Hua" last="Wang">Chun-Hua Wang</name>
</author>
<author><name sortKey="Yu, Xiao Qing" sort="Yu, Xiao Qing" uniqKey="Yu X" first="Xiao-Qing" last="Yu">Xiao-Qing Yu</name>
</author>
<author><name sortKey="Zhu, Jie" sort="Zhu, Jie" uniqKey="Zhu J" first="Jie" last="Zhu">Jie Zhu</name>
</author>
<author><name sortKey="Liu, Tai Gang" sort="Liu, Tai Gang" uniqKey="Liu T" first="Tai-Gang" last="Liu">Tai-Gang Liu</name>
</author>
<author><name sortKey="Zheng, Xiao Qi" sort="Zheng, Xiao Qi" uniqKey="Zheng X" first="Xiao-Qi" last="Zheng">Xiao-Qi Zheng</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2012">2012</date>
<idno type="RBID">pubmed:22316305</idno>
<idno type="pmid">22316305</idno>
<idno type="doi">10.2174/092986612799789350</idno>
<idno type="wicri:Area/PubMed/Corpus">001E08</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001E08</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.</title>
<author><name sortKey="Qin, Yu Fang" sort="Qin, Yu Fang" uniqKey="Qin Y" first="Yu-Fang" last="Qin">Yu-Fang Qin</name>
<affiliation><nlm:affiliation>Department of Mathematics, Shanghai Normal University, 200234 Shanghai, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wang, Chun Hua" sort="Wang, Chun Hua" uniqKey="Wang C" first="Chun-Hua" last="Wang">Chun-Hua Wang</name>
</author>
<author><name sortKey="Yu, Xiao Qing" sort="Yu, Xiao Qing" uniqKey="Yu X" first="Xiao-Qing" last="Yu">Xiao-Qing Yu</name>
</author>
<author><name sortKey="Zhu, Jie" sort="Zhu, Jie" uniqKey="Zhu J" first="Jie" last="Zhu">Jie Zhu</name>
</author>
<author><name sortKey="Liu, Tai Gang" sort="Liu, Tai Gang" uniqKey="Liu T" first="Tai-Gang" last="Liu">Tai-Gang Liu</name>
</author>
<author><name sortKey="Zheng, Xiao Qi" sort="Zheng, Xiao Qi" uniqKey="Zheng X" first="Xiao-Qi" last="Zheng">Xiao-Qi Zheng</name>
</author>
</analytic>
<series><title level="j">Protein and peptide letters</title>
<idno type="eISSN">1875-5305</idno>
<imprint><date when="2012" type="published">2012</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Amino Acids (chemistry)</term>
<term>Computational Biology</term>
<term>Databases, Protein</term>
<term>Neural Networks, Computer</term>
<term>Protein Folding</term>
<term>Protein Structure, Tertiary</term>
<term>Proteins (chemistry)</term>
<term>Proteins (classification)</term>
<term>Sequence Analysis, Protein</term>
<term>Support Vector Machine</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Amino Acids</term>
<term>Proteins</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="classification" xml:lang="en"><term>Proteins</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Computational Biology</term>
<term>Databases, Protein</term>
<term>Neural Networks, Computer</term>
<term>Protein Folding</term>
<term>Protein Structure, Tertiary</term>
<term>Sequence Analysis, Protein</term>
<term>Support Vector Machine</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. In this paper, a new feature extraction approach based on relative polypeptide composition is introduced. This approach could take into account the background distribution of a given k-mer under a Markov model of order k-2, and avoid the curse of dimensionality with the increase of k by using a T-statistic feature selection strategy. The selected features are then fed to a support vector machine to perform the prediction. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22316305</PMID>
<DateCompleted><Year>2012</Year>
<Month>08</Month>
<Day>01</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>12</Month>
<Day>10</Day>
</DateRevised>
<Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1875-5305</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>19</Volume>
<Issue>4</Issue>
<PubDate><Year>2012</Year>
<Month>Apr</Month>
</PubDate>
</JournalIssue>
<Title>Protein and peptide letters</Title>
<ISOAbbreviation>Protein Pept. Lett.</ISOAbbreviation>
</Journal>
<ArticleTitle>Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.</ArticleTitle>
<Pagination><MedlinePgn>388-97</MedlinePgn>
</Pagination>
<Abstract><AbstractText>Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. In this paper, a new feature extraction approach based on relative polypeptide composition is introduced. This approach could take into account the background distribution of a given k-mer under a Markov model of order k-2, and avoid the curse of dimensionality with the increase of k by using a T-statistic feature selection strategy. The selected features are then fed to a support vector machine to perform the prediction. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Qin</LastName>
<ForeName>Yu-Fang</ForeName>
<Initials>YF</Initials>
<AffiliationInfo><Affiliation>Department of Mathematics, Shanghai Normal University, 200234 Shanghai, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wang</LastName>
<ForeName>Chun-Hua</ForeName>
<Initials>CH</Initials>
</Author>
<Author ValidYN="Y"><LastName>Yu</LastName>
<ForeName>Xiao-Qing</ForeName>
<Initials>XQ</Initials>
</Author>
<Author ValidYN="Y"><LastName>Zhu</LastName>
<ForeName>Jie</ForeName>
<Initials>J</Initials>
</Author>
<Author ValidYN="Y"><LastName>Liu</LastName>
<ForeName>Tai-Gang</ForeName>
<Initials>TG</Initials>
</Author>
<Author ValidYN="Y"><LastName>Zheng</LastName>
<ForeName>Xiao-Qi</ForeName>
<Initials>XQ</Initials>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
</Article>
<MedlineJournalInfo><Country>Netherlands</Country>
<MedlineTA>Protein Pept Lett</MedlineTA>
<NlmUniqueID>9441434</NlmUniqueID>
<ISSNLinking>0929-8665</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D000596">Amino Acids</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D011506">Proteins</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000596" MajorTopicYN="N">Amino Acids</DescriptorName>
<QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019295" MajorTopicYN="Y">Computational Biology</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D030562" MajorTopicYN="N">Databases, Protein</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016571" MajorTopicYN="N">Neural Networks, Computer</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017510" MajorTopicYN="N">Protein Folding</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017434" MajorTopicYN="Y">Protein Structure, Tertiary</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011506" MajorTopicYN="N">Proteins</DescriptorName>
<QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName>
<QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D020539" MajorTopicYN="N">Sequence Analysis, Protein</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D060388" MajorTopicYN="N">Support Vector Machine</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2011</Year>
<Month>10</Month>
<Day>26</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="revised"><Year>2011</Year>
<Month>12</Month>
<Day>10</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2011</Year>
<Month>12</Month>
<Day>12</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2012</Year>
<Month>2</Month>
<Day>10</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2012</Year>
<Month>2</Month>
<Day>10</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2012</Year>
<Month>8</Month>
<Day>2</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">22316305</ArticleId>
<ArticleId IdType="pii">PPL-EPUB-20120207-002</ArticleId>
<ArticleId IdType="doi">10.2174/092986612799789350</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/PubMed/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001E08 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 001E08 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    MersV1
   |flux=    PubMed
   |étape=   Corpus
   |type=    RBID
   |clé=     pubmed:22316305
   |texte=   Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i   -Sk "pubmed:22316305" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd   \
       | NlmPubMed2Wicri -a MersV1

This area was generated with Dilib version V0.6.33.
Data generation: Mon Apr 20 23:26:43 2020. Site generation: Sat Mar 27 09:06:09 2021

	Serveur d'exploration MERS
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration MERS

Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.

Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki