MersV1, PubMed, Curation, bibRecord, 001554

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

Identifieur interne : 001554 ( PubMed/Curation ); précédent : 001553; suivant : 001555

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

Auteurs : Antonino Fiannaca [Italie] ; Massimo La Rosa [Italie] ; Riccardo Rizzo [Italie] ; Alfonso Urso [Italie]

Source :

Artificial intelligence in medicine [ 1873-2860 ] ; 2015.

RBID : pubmed:26170017

Descripteurs français

English descriptors

KwdEn :
- Algorithms, Animals, Base Sequence, Cluster Analysis, Computational Biology, DNA (classification), DNA (genetics), DNA Barcoding, Taxonomic (methods), Databases, Genetic, Decision Trees, Neural Networks, Computer, Reproducibility of Results, Species Specificity, Supervised Machine Learning, Support Vector Machine.
MESH :
- chemical , classification : DNA.
- chemical , genetics : DNA.
- methods : DNA Barcoding, Taxonomic.
- Algorithms, Animals, Base Sequence, Cluster Analysis, Computational Biology, Databases, Genetic, Decision Trees, Neural Networks, Computer, Reproducibility of Results, Species Specificity, Supervised Machine Learning, Support Vector Machine.

Abstract

In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed.

DOI: 10.1016/j.artmed.2015.06.002
PubMed: 26170017

Links toward previous steps (curation, corpus...)

to stream PubMed, to step Corpus: Pour aller vers cette notice dans l'étape Curation :001554

Links to Exploration step

pubmed:26170017

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.</title>
<author><name sortKey="Fiannaca, Antonino" sort="Fiannaca, Antonino" uniqKey="Fiannaca A" first="Antonino" last="Fiannaca">Antonino Fiannaca</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy. Electronic address: fiannaca@pa.icar.cnr.it.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="La Rosa, Massimo" sort="La Rosa, Massimo" uniqKey="La Rosa M" first="Massimo" last="La Rosa">Massimo La Rosa</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Rizzo, Riccardo" sort="Rizzo, Riccardo" uniqKey="Rizzo R" first="Riccardo" last="Rizzo">Riccardo Rizzo</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Urso, Alfonso" sort="Urso, Alfonso" uniqKey="Urso A" first="Alfonso" last="Urso">Alfonso Urso</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2015">2015</date>
<idno type="RBID">pubmed:26170017</idno>
<idno type="pmid">26170017</idno>
<idno type="doi">10.1016/j.artmed.2015.06.002</idno>
<idno type="wicri:Area/PubMed/Corpus">001554</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001554</idno>
<idno type="wicri:Area/PubMed/Curation">001554</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001554</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.</title>
<author><name sortKey="Fiannaca, Antonino" sort="Fiannaca, Antonino" uniqKey="Fiannaca A" first="Antonino" last="Fiannaca">Antonino Fiannaca</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy. Electronic address: fiannaca@pa.icar.cnr.it.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="La Rosa, Massimo" sort="La Rosa, Massimo" uniqKey="La Rosa M" first="Massimo" last="La Rosa">Massimo La Rosa</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Rizzo, Riccardo" sort="Rizzo, Riccardo" uniqKey="Rizzo R" first="Riccardo" last="Rizzo">Riccardo Rizzo</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Urso, Alfonso" sort="Urso, Alfonso" uniqKey="Urso A" first="Alfonso" last="Urso">Alfonso Urso</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</nlm:affiliation>
<country xml:lang="fr">Italie</country>
<wicri:regionArea>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo</wicri:regionArea>
</affiliation>
</author>
</analytic>
<series><title level="j">Artificial intelligence in medicine</title>
<idno type="eISSN">1873-2860</idno>
<imprint><date when="2015" type="published">2015</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Animals</term>
<term>Base Sequence</term>
<term>Cluster Analysis</term>
<term>Computational Biology</term>
<term>DNA (classification)</term>
<term>DNA (genetics)</term>
<term>DNA Barcoding, Taxonomic (methods)</term>
<term>Databases, Genetic</term>
<term>Decision Trees</term>
<term>Neural Networks, Computer</term>
<term>Reproducibility of Results</term>
<term>Species Specificity</term>
<term>Supervised Machine Learning</term>
<term>Support Vector Machine</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>ADN ()</term>
<term>ADN (génétique)</term>
<term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Animaux</term>
<term>Apprentissage machine supervisé</term>
<term>Arbres de décision</term>
<term>Bases de données génétiques</term>
<term>Biologie informatique</term>
<term>Codage à barres de l'ADN pour la taxonomie ()</term>
<term>Machine à vecteur de support</term>
<term>Reproductibilité des résultats</term>
<term>Spécificité d'espèce</term>
<term>Séquence nucléotidique</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="classification" xml:lang="en"><term>DNA</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>DNA</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ADN</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>DNA Barcoding, Taxonomic</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Animals</term>
<term>Base Sequence</term>
<term>Cluster Analysis</term>
<term>Computational Biology</term>
<term>Databases, Genetic</term>
<term>Decision Trees</term>
<term>Neural Networks, Computer</term>
<term>Reproducibility of Results</term>
<term>Species Specificity</term>
<term>Supervised Machine Learning</term>
<term>Support Vector Machine</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>ADN</term>
<term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Animaux</term>
<term>Apprentissage machine supervisé</term>
<term>Arbres de décision</term>
<term>Bases de données génétiques</term>
<term>Biologie informatique</term>
<term>Codage à barres de l'ADN pour la taxonomie</term>
<term>Machine à vecteur de support</term>
<term>Reproductibilité des résultats</term>
<term>Spécificité d'espèce</term>
<term>Séquence nucléotidique</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26170017</PMID>
<DateCompleted><Year>2016</Year>
<Month>06</Month>
<Day>29</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>12</Month>
<Day>10</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-2860</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>64</Volume>
<Issue>3</Issue>
<PubDate><Year>2015</Year>
<Month>Jul</Month>
</PubDate>
</JournalIssue>
<Title>Artificial intelligence in medicine</Title>
<ISOAbbreviation>Artif Intell Med</ISOAbbreviation>
</Journal>
<ArticleTitle>A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.</ArticleTitle>
<Pagination><MedlinePgn>173-84</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1016/j.artmed.2015.06.002</ELocationID>
<ELocationID EIdType="pii" ValidYN="Y">S0933-3657(15)00081-0</ELocationID>
<Abstract><AbstractText Label="OBJECTIVES" NlmCategory="OBJECTIVE">In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database".</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments.</AbstractText>
<CopyrightInformation>Copyright © 2015 Elsevier B.V. All rights reserved.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fiannaca</LastName>
<ForeName>Antonino</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy. Electronic address: fiannaca@pa.icar.cnr.it.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>La Rosa</LastName>
<ForeName>Massimo</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Rizzo</LastName>
<ForeName>Riccardo</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Urso</LastName>
<ForeName>Alfonso</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType>
<PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2015</Year>
<Month>07</Month>
<Day>04</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>Netherlands</Country>
<MedlineTA>Artif Intell Med</MedlineTA>
<NlmUniqueID>8915031</NlmUniqueID>
<ISSNLinking>0933-3657</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>9007-49-2</RegistryNumber>
<NameOfSubstance UI="D004247">DNA</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D001483" MajorTopicYN="N">Base Sequence</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D004247" MajorTopicYN="N">DNA</DescriptorName>
<QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058893" MajorTopicYN="N">DNA Barcoding, Taxonomic</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D030541" MajorTopicYN="N">Databases, Genetic</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003663" MajorTopicYN="N">Decision Trees</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016571" MajorTopicYN="Y">Neural Networks, Computer</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D013045" MajorTopicYN="N">Species Specificity</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000069553" MajorTopicYN="Y">Supervised Machine Learning</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D060388" MajorTopicYN="N">Support Vector Machine</DescriptorName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Alignment-free analysis</Keyword>
<Keyword MajorTopicYN="N">DNA barcode classification</Keyword>
<Keyword MajorTopicYN="N">Neural gas</Keyword>
<Keyword MajorTopicYN="N">k-Mer representation</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year>
<Month>08</Month>
<Day>27</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="revised"><Year>2015</Year>
<Month>05</Month>
<Day>25</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2015</Year>
<Month>06</Month>
<Day>25</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2015</Year>
<Month>7</Month>
<Day>15</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2015</Year>
<Month>7</Month>
<Day>15</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2016</Year>
<Month>6</Month>
<Day>30</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">26170017</ArticleId>
<ArticleId IdType="pii">S0933-3657(15)00081-0</ArticleId>
<ArticleId IdType="doi">10.1016/j.artmed.2015.06.002</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
</record>

Pour manipuler ce document sous Unix (Dilib)

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

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001554 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd -nk 001554 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    MersV1
   |flux=    PubMed
   |étape=   Curation
   |type=    RBID
   |clé=     pubmed:26170017
   |texte=   A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Curation/RBID.i   -Sk "pubmed:26170017" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Curation/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

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

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