Using cellular automata to generate image representation for biological sequences.
Identifieur interne : 002899 ( PubMed/Corpus ); précédent : 002898; suivant : 002900Using cellular automata to generate image representation for biological sequences.
Auteurs : X. Xiao ; S. Shao ; Y. Ding ; Z. Huang ; X. Chen ; K-C ChouSource :
- Amino acids [ 0939-4451 ] ; 2005.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : Transforming Growth Factor alpha.
- genetics : Coronavirus, Hepatitis B, SARS Virus.
- methods : Image Processing, Computer-Assisted.
- Animals, Database Management Systems, Databases, Genetic, Mice, Sequence Homology, Nucleic Acid, Software.
Abstract
A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.
DOI: 10.1007/s00726-004-0154-9
PubMed: 15700108
Links to Exploration step
pubmed:15700108Le document en format XML
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Chen</name></author><author><name sortKey="Chou, K C" sort="Chou, K C" uniqKey="Chou K" first="K-C" last="Chou">K-C Chou</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2005">2005</date><idno type="RBID">pubmed:15700108</idno><idno type="pmid">15700108</idno><idno type="doi">10.1007/s00726-004-0154-9</idno><idno type="wicri:Area/PubMed/Corpus">002899</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">002899</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Using cellular automata to generate image representation for biological sequences.</title><author><name sortKey="Xiao, X" sort="Xiao, X" uniqKey="Xiao X" first="X" last="Xiao">X. Xiao</name><affiliation><nlm:affiliation>Bio-Informatics Research Center, Donghua University, Shanghai, China.</nlm:affiliation></affiliation></author><author><name sortKey="Shao, S" sort="Shao, S" uniqKey="Shao S" first="S" last="Shao">S. Shao</name></author><author><name sortKey="Ding, Y" sort="Ding, Y" uniqKey="Ding Y" first="Y" last="Ding">Y. Ding</name></author><author><name sortKey="Huang, Z" sort="Huang, Z" uniqKey="Huang Z" first="Z" last="Huang">Z. Huang</name></author><author><name sortKey="Chen, X" sort="Chen, X" uniqKey="Chen X" first="X" last="Chen">X. Chen</name></author><author><name sortKey="Chou, K C" sort="Chou, K C" uniqKey="Chou K" first="K-C" last="Chou">K-C Chou</name></author></analytic><series><title level="j">Amino acids</title><idno type="ISSN">0939-4451</idno><imprint><date when="2005" type="published">2005</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Coronavirus (genetics)</term><term>Database Management Systems</term><term>Databases, Genetic</term><term>Hepatitis B (genetics)</term><term>Image Processing, Computer-Assisted (methods)</term><term>Mice</term><term>SARS Virus (genetics)</term><term>Sequence Homology, Nucleic Acid</term><term>Software</term><term>Transforming Growth Factor alpha (genetics)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Transforming Growth Factor alpha</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Coronavirus</term><term>Hepatitis B</term><term>SARS Virus</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Database Management Systems</term><term>Databases, Genetic</term><term>Mice</term><term>Sequence Homology, Nucleic Acid</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">15700108</PMID><DateCompleted><Year>2005</Year><Month>08</Month><Day>30</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0939-4451</ISSN><JournalIssue CitedMedium="Print"><Volume>28</Volume><Issue>1</Issue><PubDate><Year>2005</Year><Month>Feb</Month></PubDate></JournalIssue><Title>Amino acids</Title><ISOAbbreviation>Amino Acids</ISOAbbreviation></Journal><ArticleTitle>Using cellular automata to generate image representation for biological sequences.</ArticleTitle><Pagination><MedlinePgn>29-35</MedlinePgn></Pagination><Abstract><AbstractText>A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Xiao</LastName><ForeName>X</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Bio-Informatics Research Center, Donghua University, Shanghai, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shao</LastName><ForeName>S</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Ding</LastName><ForeName>Y</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Z</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>X</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Chou</LastName><ForeName>K-C</ForeName><Initials>KC</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><ArticleDate DateType="Electronic"><Year>2005</Year><Month>02</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>Austria</Country><MedlineTA>Amino Acids</MedlineTA><NlmUniqueID>9200312</NlmUniqueID><ISSNLinking>0939-4451</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D016211">Transforming Growth Factor alpha</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017934" MajorTopicYN="N">Coronavirus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003628" MajorTopicYN="N">Database Management Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D030541" MajorTopicYN="Y">Databases, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006509" MajorTopicYN="N">Hepatitis B</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007091" MajorTopicYN="N">Image Processing, Computer-Assisted</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D051379" MajorTopicYN="N">Mice</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045473" MajorTopicYN="N">SARS Virus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012689" MajorTopicYN="N">Sequence Homology, Nucleic Acid</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016211" MajorTopicYN="N">Transforming Growth Factor alpha</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2004</Year><Month>10</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2004</Year><Month>12</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2005</Year><Month>2</Month><Day>9</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2005</Year><Month>9</Month><Day>1</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2005</Year><Month>2</Month><Day>9</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">15700108</ArticleId><ArticleId IdType="doi">10.1007/s00726-004-0154-9</ArticleId><ArticleId IdType="pmc">PMC7088382</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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