Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.
Identifieur interne : 000337 ( Ncbi/Checkpoint ); précédent : 000336; suivant : 000338Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.
Auteurs : Zheng Rong Yang [Royaume-Uni]Source :
- Bioinformatics (Oxford, England) [ 1367-4803 ] ; 2005.
Descripteurs français
- KwdFr :
- Algorithmes, Alignement de séquences (), Analyse de séquence de protéine (), Bases de données de protéines, Cysteine endopeptidases, Endopeptidases (), Endopeptidases (analyse), Intelligence artificielle, Liaison aux protéines, Modèles chimiques, Modèles moléculaires, Protéines virales (), Protéines virales (analyse), Similitude de séquences d'acides aminés, Simulation numérique, Sites de fixation, Techniques d'aide à la décision.
- MESH :
- analyse : Endopeptidases, Protéines virales.
- Algorithmes, Alignement de séquences, Analyse de séquence de protéine, Bases de données de protéines, Cysteine endopeptidases, Endopeptidases, Intelligence artificielle, Liaison aux protéines, Modèles chimiques, Modèles moléculaires, Protéines virales, Similitude de séquences d'acides aminés, Simulation numérique, Sites de fixation, Techniques d'aide à la décision.
English descriptors
- KwdEn :
- Algorithms, Artificial Intelligence, Binding Sites, Computer Simulation, Cysteine Endopeptidases, Databases, Protein, Decision Support Techniques, Endopeptidases (analysis), Endopeptidases (chemistry), Models, Chemical, Models, Molecular, Protein Binding, Sequence Alignment (methods), Sequence Analysis, Protein (methods), Sequence Homology, Amino Acid, Viral Proteins (analysis), Viral Proteins (chemistry).
- MESH :
- chemical , analysis : Endopeptidases, Viral Proteins.
- chemical , chemistry : Endopeptidases, Viral Proteins.
- chemical : Cysteine Endopeptidases.
- methods : Sequence Alignment, Sequence Analysis, Protein.
- Algorithms, Artificial Intelligence, Binding Sites, Computer Simulation, Databases, Protein, Decision Support Techniques, Models, Chemical, Models, Molecular, Protein Binding, Sequence Homology, Amino Acid.
Abstract
Although the outbreak of the severe acute respiratory syndrome (SARS) is currently over, it is expected that it will return to attack human beings. A critical challenge to scientists from various disciplines worldwide is to study the specificity of cleavage activity of SARS-related coronavirus (SARS-CoV) and use the knowledge obtained from the study for effective inhibitor design to fight the disease. The most commonly used inductive programming methods for knowledge discovery from data assume that the elements of input patterns are orthogonal to each other. Suppose a sub-sequence is denoted as P2-P1-P1'-P2', the conventional inductive programming method may result in a rule like 'if P1 = Q, then the sub-sequence is cleaved, otherwise non-cleaved'. If the site P1 is not orthogonal to the others (for instance, P2, P1' and P2'), the prediction power of these kind of rules may be limited. Therefore this study is aimed at developing a novel method for constructing non-orthogonal decision trees for mining protease data.
DOI: 10.1093/bioinformatics/bti404
PubMed: 15797903
Affiliations:
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z.r.yang@exeter.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Computer Science, Exeter University</wicri:regionArea><wicri:noRegion>Exeter University</wicri:noRegion></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="ISSN">1367-4803</idno><imprint><date when="2005" type="published">2005</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Binding Sites</term><term>Computer Simulation</term><term>Cysteine Endopeptidases</term><term>Databases, Protein</term><term>Decision Support Techniques</term><term>Endopeptidases (analysis)</term><term>Endopeptidases (chemistry)</term><term>Models, Chemical</term><term>Models, Molecular</term><term>Protein Binding</term><term>Sequence Alignment (methods)</term><term>Sequence Analysis, Protein (methods)</term><term>Sequence Homology, Amino Acid</term><term>Viral Proteins (analysis)</term><term>Viral Proteins (chemistry)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences ()</term><term>Analyse de séquence de protéine ()</term><term>Bases de données de protéines</term><term>Cysteine endopeptidases</term><term>Endopeptidases ()</term><term>Endopeptidases (analyse)</term><term>Intelligence artificielle</term><term>Liaison aux protéines</term><term>Modèles chimiques</term><term>Modèles moléculaires</term><term>Protéines virales ()</term><term>Protéines virales (analyse)</term><term>Similitude de séquences d'acides aminés</term><term>Simulation numérique</term><term>Sites de fixation</term><term>Techniques d'aide à la décision</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Endopeptidases</term><term>Viral Proteins</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Endopeptidases</term><term>Viral Proteins</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Cysteine Endopeptidases</term></keywords><keywords scheme="MESH" qualifier="analyse" xml:lang="fr"><term>Endopeptidases</term><term>Protéines virales</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Sequence Alignment</term><term>Sequence Analysis, Protein</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Binding Sites</term><term>Computer Simulation</term><term>Databases, Protein</term><term>Decision Support Techniques</term><term>Models, Chemical</term><term>Models, Molecular</term><term>Protein Binding</term><term>Sequence Homology, Amino Acid</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence de protéine</term><term>Bases de données de protéines</term><term>Cysteine endopeptidases</term><term>Endopeptidases</term><term>Intelligence artificielle</term><term>Liaison aux protéines</term><term>Modèles chimiques</term><term>Modèles moléculaires</term><term>Protéines virales</term><term>Similitude de séquences d'acides aminés</term><term>Simulation numérique</term><term>Sites de fixation</term><term>Techniques d'aide à la décision</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Although the outbreak of the severe acute respiratory syndrome (SARS) is currently over, it is expected that it will return to attack human beings. A critical challenge to scientists from various disciplines worldwide is to study the specificity of cleavage activity of SARS-related coronavirus (SARS-CoV) and use the knowledge obtained from the study for effective inhibitor design to fight the disease. The most commonly used inductive programming methods for knowledge discovery from data assume that the elements of input patterns are orthogonal to each other. Suppose a sub-sequence is denoted as P2-P1-P1'-P2', the conventional inductive programming method may result in a rule like 'if P1 = Q, then the sub-sequence is cleaved, otherwise non-cleaved'. If the site P1 is not orthogonal to the others (for instance, P2, P1' and P2'), the prediction power of these kind of rules may be limited. Therefore this study is aimed at developing a novel method for constructing non-orthogonal decision trees for mining protease data.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li></country></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="Yang, Zheng Rong" sort="Yang, Zheng Rong" uniqKey="Yang Z" first="Zheng Rong" last="Yang">Zheng Rong Yang</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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