MersV1, PubMed, Corpus, bibRecord, 001910

Enhanced regulatory sequence prediction using gapped k-mer features.

Identifieur interne : 001910 ( PubMed/Corpus ); précédent : 001909; suivant : 001911

Enhanced regulatory sequence prediction using gapped k-mer features.

Auteurs : Mahmoud Ghandi ; Dongwon Lee ; Morteza Mohammad-Noori ; Michael A. Beer

Source :

PLoS computational biology [ 1553-7358 ] ; 2014.

RBID : pubmed:25033408

English descriptors

KwdEn :
- Base Sequence, Bayes Theorem, Chromatin Immunoprecipitation, Computational Biology (methods), Models, Genetic, Oligonucleotides (genetics), Organ Specificity (genetics), Regulatory Sequences, Nucleic Acid (genetics), Sequence Analysis, DNA (methods), Support Vector Machine.
MESH :
- chemical , genetics : Oligonucleotides.
- genetics : Organ Specificity, Regulatory Sequences, Nucleic Acid.
- methods : Computational Biology, Sequence Analysis, DNA.
- Base Sequence, Bayes Theorem, Chromatin Immunoprecipitation, Models, Genetic, Support Vector Machine.

Abstract

Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem.

DOI: 10.1371/journal.pcbi.1003711
PubMed: 25033408

Links to Exploration step

pubmed:25033408

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Enhanced regulatory sequence prediction using gapped k-mer features.</title>
<author><name sortKey="Ghandi, Mahmoud" sort="Ghandi, Mahmoud" uniqKey="Ghandi M" first="Mahmoud" last="Ghandi">Mahmoud Ghandi</name>
<affiliation><nlm:affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Lee, Dongwon" sort="Lee, Dongwon" uniqKey="Lee D" first="Dongwon" last="Lee">Dongwon Lee</name>
<affiliation><nlm:affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Mohammad Noori, Morteza" sort="Mohammad Noori, Morteza" uniqKey="Mohammad Noori M" first="Morteza" last="Mohammad-Noori">Morteza Mohammad-Noori</name>
<affiliation><nlm:affiliation>School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Beer, Michael A" sort="Beer, Michael A" uniqKey="Beer M" first="Michael A" last="Beer">Michael A. Beer</name>
<affiliation><nlm:affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.</nlm:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2014">2014</date>
<idno type="RBID">pubmed:25033408</idno>
<idno type="pmid">25033408</idno>
<idno type="doi">10.1371/journal.pcbi.1003711</idno>
<idno type="wicri:Area/PubMed/Corpus">001910</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001910</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Enhanced regulatory sequence prediction using gapped k-mer features.</title>
<author><name sortKey="Ghandi, Mahmoud" sort="Ghandi, Mahmoud" uniqKey="Ghandi M" first="Mahmoud" last="Ghandi">Mahmoud Ghandi</name>
<affiliation><nlm:affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Lee, Dongwon" sort="Lee, Dongwon" uniqKey="Lee D" first="Dongwon" last="Lee">Dongwon Lee</name>
<affiliation><nlm:affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Mohammad Noori, Morteza" sort="Mohammad Noori, Morteza" uniqKey="Mohammad Noori M" first="Morteza" last="Mohammad-Noori">Morteza Mohammad-Noori</name>
<affiliation><nlm:affiliation>School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Beer, Michael A" sort="Beer, Michael A" uniqKey="Beer M" first="Michael A" last="Beer">Michael A. Beer</name>
<affiliation><nlm:affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.</nlm:affiliation>
</affiliation>
</author>
</analytic>
<series><title level="j">PLoS computational biology</title>
<idno type="eISSN">1553-7358</idno>
<imprint><date when="2014" type="published">2014</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Base Sequence</term>
<term>Bayes Theorem</term>
<term>Chromatin Immunoprecipitation</term>
<term>Computational Biology (methods)</term>
<term>Models, Genetic</term>
<term>Oligonucleotides (genetics)</term>
<term>Organ Specificity (genetics)</term>
<term>Regulatory Sequences, Nucleic Acid (genetics)</term>
<term>Sequence Analysis, DNA (methods)</term>
<term>Support Vector Machine</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Oligonucleotides</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Organ Specificity</term>
<term>Regulatory Sequences, Nucleic Acid</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
<term>Sequence Analysis, DNA</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Base Sequence</term>
<term>Bayes Theorem</term>
<term>Chromatin Immunoprecipitation</term>
<term>Models, Genetic</term>
<term>Support Vector Machine</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. </div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25033408</PMID>
<DateCompleted><Year>2015</Year>
<Month>03</Month>
<Day>02</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>02</Month>
<Day>01</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>10</Volume>
<Issue>7</Issue>
<PubDate><Year>2014</Year>
<Month>Jul</Month>
</PubDate>
</JournalIssue>
<Title>PLoS computational biology</Title>
<ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation>
</Journal>
<ArticleTitle>Enhanced regulatory sequence prediction using gapped k-mer features.</ArticleTitle>
<Pagination><MedlinePgn>e1003711</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1003711</ELocationID>
<Abstract><AbstractText>Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. </AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ghandi</LastName>
<ForeName>Mahmoud</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lee</LastName>
<ForeName>Dongwon</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mohammad-Noori</LastName>
<ForeName>Morteza</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran; School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Beer</LastName>
<ForeName>Michael A</ForeName>
<Initials>MA</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>R01 HG007348</GrantID>
<Acronym>HG</Acronym>
<Agency>NHGRI NIH HHS</Agency>
<Country>United States</Country>
</Grant>
</GrantList>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2014</Year>
<Month>07</Month>
<Day>17</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>PLoS Comput Biol</MedlineTA>
<NlmUniqueID>101238922</NlmUniqueID>
<ISSNLinking>1553-734X</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D009841">Oligonucleotides</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<CommentsCorrectionsList><CommentsCorrections RefType="ErratumIn"><RefSource>PLoS Comput Biol. 2014 Dec;10(12):e1004035</RefSource>
</CommentsCorrections>
</CommentsCorrectionsList>
<MeshHeadingList><MeshHeading><DescriptorName UI="D001483" MajorTopicYN="N">Base Sequence</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D047369" MajorTopicYN="N">Chromatin Immunoprecipitation</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008957" MajorTopicYN="Y">Models, Genetic</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D009841" MajorTopicYN="N">Oligonucleotides</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D009928" MajorTopicYN="N">Organ Specificity</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012045" MajorTopicYN="N">Regulatory Sequences, Nucleic Acid</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D060388" MajorTopicYN="N">Support Vector Machine</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year>
<Month>08</Month>
<Day>01</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2014</Year>
<Month>05</Month>
<Day>28</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2014</Year>
<Month>7</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2014</Year>
<Month>7</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2015</Year>
<Month>3</Month>
<Day>3</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">25033408</ArticleId>
<ArticleId IdType="doi">10.1371/journal.pcbi.1003711</ArticleId>
<ArticleId IdType="pii">PCOMPBIOL-D-13-01373</ArticleId>
<ArticleId IdType="pmc">PMC4102394</ArticleId>
</ArticleIdList>
<ReferenceList><Reference><Citation>Nature. 2012 Sep 6;489(7414):91-100</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22955619</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2000 Jan;16(1):16-23</Citation>
<ArticleIdList><ArticleId IdType="pubmed">10812473</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nat Biotechnol. 2006 Nov;24(11):1429-35</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16998473</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nat Biotechnol. 2013 Feb;31(2):126-34</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23354101</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>PLoS Comput Biol. 2010;6(9). pii: e1000916. doi: 10.1371/journal.pcbi.1000916</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20838582</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2004 Mar 1;20(4):467-76</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14990442</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Res. 2011 Dec;21(12):2167-80</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21875935</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2011 Jun 15;27(12):1696-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21486936</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Math Biol. 2014 Aug;69(2):469-500</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23861010</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Proteomics Bioinform. 2011;4(2):22-35</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21720494</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2005 Oct 15;21(20):3940-1</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16096348</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2008 Jan;36(Database issue):D102-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18006571</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nature. 1991 Jan 17;349(6306):257-60</Citation>
<ArticleIdList><ArticleId IdType="pubmed">1987478</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Biol. 2008;9(9):R137</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18798982</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Res. 2012 Sep;22(9):1723-34</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22955984</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Cell. 2007 Mar 23;128(6):1231-45</Citation>
<ArticleIdList><ArticleId IdType="pubmed">17382889</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>N Engl J Med. 2010 Jul 8;363(2):166-76</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20647212</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Res. 2001 Aug;11(8):1404-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11483581</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Int J Biochem Cell Biol. 2001 Apr;33(4):391-407</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11312108</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Science. 2010 Apr 9;328(5975):235-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20299549</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nature. 2009 Feb 12;457(7231):854-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19212405</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2013 Jul;41(Web Server issue):W544-56</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23771147</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Cell. 2008 Feb 8;132(3):422-33</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18237772</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Res. 2012 Nov;22(11):2290-301</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23019145</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Pac Symp Biocomput. 2002;:564-75</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11928508</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Comput Biol. 2000 Feb-Apr;7(1-2):95-114</Citation>
<ArticleIdList><ArticleId IdType="pubmed">10890390</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Biol. 2007;8(2):R24</Citation>
<ArticleIdList><ArticleId IdType="pubmed">17324271</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Science. 2012 Sep 7;337(6099):1190-5</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22955828</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Cell. 2004 Apr 16;117(2):185-98</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15084257</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Res. 2012 Sep;22(9):1798-812</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22955990</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</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 001910 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 001910 | 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:25033408
   |texte=   Enhanced regulatory sequence prediction using gapped k-mer features.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i   -Sk "pubmed:25033408" \
       | 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

Enhanced regulatory sequence prediction using gapped k-mer features.

Enhanced regulatory sequence prediction using gapped k-mer features.

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