Modelling complex features from histone modification signatures using genetic algorithm for the prediction of enhancer region.
Identifieur interne : 001849 ( PubMed/Curation ); précédent : 001848; suivant : 001850Modelling complex features from histone modification signatures using genetic algorithm for the prediction of enhancer region.
Auteurs : Nung Kion Lee [Malaisie] ; Pui Kwan Fong [Malaisie] ; Mohd Tajuddin Abdullah [Malaisie]Source :
- Bio-medical materials and engineering [ 1878-3619 ] ; 2014.
Descripteurs français
- KwdFr :
- Algorithmes, Analyse de séquence d'ADN (), Animaux, Cartographie chromosomique (), Données de séquences moléculaires, Histone (génétique), Modèles génétiques, Reconnaissance automatique des formes (), Simulation numérique, Souris, Séquence nucléotidique, Éléments activateurs (génétique) (génétique), Épigenèse génétique (génétique).
- MESH :
- génétique : Histone, Éléments activateurs (génétique), Épigenèse génétique.
- Algorithmes, Analyse de séquence d'ADN, Animaux, Cartographie chromosomique, Données de séquences moléculaires, Modèles génétiques, Reconnaissance automatique des formes, Simulation numérique, Souris, Séquence nucléotidique.
English descriptors
- KwdEn :
- Algorithms, Animals, Base Sequence, Chromosome Mapping (methods), Computer Simulation, Enhancer Elements, Genetic (genetics), Epigenesis, Genetic (genetics), Histones (genetics), Mice, Models, Genetic, Molecular Sequence Data, Pattern Recognition, Automated (methods), Sequence Analysis, DNA (methods).
- MESH :
- chemical , genetics : Histones.
- genetics : Enhancer Elements, Genetic, Epigenesis, Genetic.
- methods : Chromosome Mapping, Pattern Recognition, Automated, Sequence Analysis, DNA.
- Algorithms, Animals, Base Sequence, Computer Simulation, Mice, Models, Genetic, Molecular Sequence Data.
Abstract
Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features.
DOI: 10.3233/BME-141210
PubMed: 25227097
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xml:lang="fr">Malaisie</country><wicri:regionArea>Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kota Samarahan</wicri:regionArea></affiliation></author><author><name sortKey="Abdullah, Mohd Tajuddin" sort="Abdullah, Mohd Tajuddin" uniqKey="Abdullah M" first="Mohd Tajuddin" last="Abdullah">Mohd Tajuddin Abdullah</name><affiliation wicri:level="1"><nlm:affiliation>Center of Tasik Kenyir Ecosystem, Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia.</nlm:affiliation><country xml:lang="fr">Malaisie</country><wicri:regionArea>Center of Tasik Kenyir Ecosystem, Universiti Malaysia Terengganu, Kuala Terengganu</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25227097</idno><idno type="pmid">25227097</idno><idno type="doi">10.3233/BME-141210</idno><idno type="wicri:Area/PubMed/Corpus">001849</idno><idno type="wicri:explorRef" 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first="Pui Kwan" last="Fong">Pui Kwan Fong</name><affiliation wicri:level="1"><nlm:affiliation>Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia.</nlm:affiliation><country xml:lang="fr">Malaisie</country><wicri:regionArea>Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kota Samarahan</wicri:regionArea></affiliation></author><author><name sortKey="Abdullah, Mohd Tajuddin" sort="Abdullah, Mohd Tajuddin" uniqKey="Abdullah M" first="Mohd Tajuddin" last="Abdullah">Mohd Tajuddin Abdullah</name><affiliation wicri:level="1"><nlm:affiliation>Center of Tasik Kenyir Ecosystem, Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia.</nlm:affiliation><country xml:lang="fr">Malaisie</country><wicri:regionArea>Center of Tasik Kenyir Ecosystem, Universiti Malaysia Terengganu, Kuala Terengganu</wicri:regionArea></affiliation></author></analytic><series><title level="j">Bio-medical materials and engineering</title><idno type="eISSN">1878-3619</idno><imprint><date when="2014" type="published">2014</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>Chromosome Mapping (methods)</term><term>Computer Simulation</term><term>Enhancer Elements, Genetic (genetics)</term><term>Epigenesis, Genetic (genetics)</term><term>Histones (genetics)</term><term>Mice</term><term>Models, Genetic</term><term>Molecular Sequence Data</term><term>Pattern Recognition, Automated (methods)</term><term>Sequence Analysis, DNA (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN ()</term><term>Animaux</term><term>Cartographie chromosomique ()</term><term>Données de séquences moléculaires</term><term>Histone (génétique)</term><term>Modèles génétiques</term><term>Reconnaissance automatique des formes ()</term><term>Simulation numérique</term><term>Souris</term><term>Séquence nucléotidique</term><term>Éléments activateurs (génétique) (génétique)</term><term>Épigenèse génétique (génétique)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Histones</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Enhancer Elements, Genetic</term><term>Epigenesis, Genetic</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Histone</term><term>Éléments activateurs (génétique)</term><term>Épigenèse génétique</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Chromosome Mapping</term><term>Pattern Recognition, Automated</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Base Sequence</term><term>Computer Simulation</term><term>Mice</term><term>Models, Genetic</term><term>Molecular Sequence Data</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Animaux</term><term>Cartographie chromosomique</term><term>Données de séquences moléculaires</term><term>Modèles génétiques</term><term>Reconnaissance automatique des formes</term><term>Simulation numérique</term><term>Souris</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25227097</PMID><DateCompleted><Year>2015</Year><Month>06</Month><Day>24</Day></DateCompleted><DateRevised><Year>2014</Year><Month>09</Month><Day>17</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1878-3619</ISSN><JournalIssue CitedMedium="Internet"><Volume>24</Volume><Issue>6</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>Bio-medical materials and engineering</Title><ISOAbbreviation>Biomed Mater Eng</ISOAbbreviation></Journal><ArticleTitle>Modelling complex features from histone modification signatures using genetic algorithm for the prediction of enhancer region.</ArticleTitle><Pagination><MedlinePgn>3807-14</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3233/BME-141210</ELocationID><Abstract><AbstractText>Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>Nung Kion</ForeName><Initials>NK</Initials><AffiliationInfo><Affiliation>Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fong</LastName><ForeName>Pui Kwan</ForeName><Initials>PK</Initials><AffiliationInfo><Affiliation>Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Abdullah</LastName><ForeName>Mohd Tajuddin</ForeName><Initials>MT</Initials><AffiliationInfo><Affiliation>Center of Tasik Kenyir Ecosystem, Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia.</Affiliation></AffiliationInfo></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>Biomed Mater Eng</MedlineTA><NlmUniqueID>9104021</NlmUniqueID><ISSNLinking>0959-2989</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D006657">Histones</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">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="D002874" MajorTopicYN="N">Chromosome Mapping</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004742" MajorTopicYN="N">Enhancer Elements, Genetic</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D044127" MajorTopicYN="N">Epigenesis, Genetic</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006657" MajorTopicYN="N">Histones</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D051379" MajorTopicYN="N">Mice</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008957" MajorTopicYN="Y">Models, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008969" MajorTopicYN="N">Molecular Sequence Data</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010363" MajorTopicYN="N">Pattern Recognition, Automated</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Genetic algorithm</Keyword><Keyword MajorTopicYN="N">histone feature</Keyword><Keyword MajorTopicYN="N">tree-based feature</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>9</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>9</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>6</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25227097</ArticleId><ArticleId IdType="pii">777638368Q3P3361</ArticleId><ArticleId IdType="doi">10.3233/BME-141210</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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