Using cellular automata to generate image representation for biological sequences.
Identifieur interne : 004569 ( Main/Exploration ); précédent : 004568; suivant : 004570Using cellular automata to generate image representation for biological sequences.
Auteurs : X. Xiao [République populaire de Chine] ; S. Shao ; Y. Ding ; Z. Huang ; X. Chen ; K-C ChouSource :
- Amino acids [ 0939-4451 ] ; 2005.
Descripteurs français
- KwdFr :
- Animaux, Bases de données génétiques, Coronavirus (génétique), Facteur de croissance transformant alpha (génétique), Hépatite B (génétique), Logiciel, Similitude de séquences d'acides nucléiques, Souris, Systèmes de gestion de bases de données, Traitement d'image par ordinateur (), Virus du SRAS (génétique).
- MESH :
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
Affiliations:
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Xiao</name><affiliation wicri:level="4"><nlm:affiliation>Bio-Informatics Research Center, Donghua University, Shanghai, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Bio-Informatics Research Center, Donghua University, Shanghai</wicri:regionArea><orgName type="university">Université Donghua</orgName><placeName><settlement type="city">Shanghai</settlement><region type="province">Shanghai</region></placeName></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="KwdFr" xml:lang="fr"><term>Animaux</term><term>Bases de données génétiques</term><term>Coronavirus (génétique)</term><term>Facteur de croissance transformant alpha (génétique)</term><term>Hépatite B (génétique)</term><term>Logiciel</term><term>Similitude de séquences d'acides nucléiques</term><term>Souris</term><term>Systèmes de gestion de bases de données</term><term>Traitement d'image par ordinateur ()</term><term>Virus du SRAS (génétique)</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="génétique" xml:lang="fr"><term>Coronavirus</term><term>Facteur de croissance transformant alpha</term><term>Hépatite B</term><term>Virus du SRAS</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><keywords scheme="MESH" xml:lang="fr"><term>Animaux</term><term>Bases de données génétiques</term><term>Logiciel</term><term>Similitude de séquences d'acides nucléiques</term><term>Souris</term><term>Systèmes de gestion de bases de données</term><term>Traitement d'image par ordinateur</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><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Shanghai</li></region><settlement><li>Shanghai</li></settlement><orgName><li>Université Donghua</li></orgName></list><tree><noCountry><name sortKey="Chen, X" sort="Chen, X" uniqKey="Chen X" first="X" last="Chen">X. Chen</name><name sortKey="Chou, K C" sort="Chou, K C" uniqKey="Chou K" first="K-C" last="Chou">K-C Chou</name><name sortKey="Ding, Y" sort="Ding, Y" uniqKey="Ding Y" first="Y" last="Ding">Y. Ding</name><name sortKey="Huang, Z" sort="Huang, Z" uniqKey="Huang Z" first="Z" last="Huang">Z. Huang</name><name sortKey="Shao, S" sort="Shao, S" uniqKey="Shao S" first="S" last="Shao">S. Shao</name></noCountry><country name="République populaire de Chine"><region name="Shanghai"><name sortKey="Xiao, X" sort="Xiao, X" uniqKey="Xiao X" first="X" last="Xiao">X. Xiao</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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