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Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.

Identifieur interne : 002341 ( PubMed/Curation ); précédent : 002340; suivant : 002342

Mining 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.

RBID : pubmed:15797903

Descripteurs français

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

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Le document en format XML

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<author><name sortKey="Yang, Zheng Rong" sort="Yang, Zheng Rong" uniqKey="Yang Z" first="Zheng Rong" last="Yang">Zheng Rong Yang</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science, Exeter University, United Kingdom. z.r.yang@exeter.ac.uk</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
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<term>Artificial Intelligence</term>
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<term>Cysteine Endopeptidases</term>
<term>Databases, Protein</term>
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<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>
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<term>Sites de fixation</term>
<term>Techniques d'aide à la décision</term>
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<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>
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<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>
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<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>
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<term>Alignement de séquences</term>
<term>Analyse de séquence de protéine</term>
<term>Bases de données de protéines</term>
<term>Cysteine endopeptidases</term>
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<term>Modèles moléculaires</term>
<term>Protéines virales</term>
<term>Similitude de séquences d'acides aminés</term>
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<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>
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<Month>09</Month>
<Day>16</Day>
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<DateRevised><Year>2020</Year>
<Month>03</Month>
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<Title>Bioinformatics (Oxford, England)</Title>
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<ArticleTitle>Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.</ArticleTitle>
<Pagination><MedlinePgn>2644-50</MedlinePgn>
</Pagination>
<Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">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.</AbstractText>
<AbstractText Label="RESULT" NlmCategory="RESULTS">Eighteen sequences of coronavirus polyprotein were downloaded from NCBI (http://www.ncbi.nlm.nih.gov). Among these sequences, 252 cleavage sites were experimentally determined. These sequences were scanned using a sliding window with size k to generate about 50,000 k-mer sub-sequences (for short, k-mers). The value of k varies from 4 to 12 with a gap of two. The bio-basis function proposed by Thomson et al. is used to transform the k-mers to a high-dimensional numerical space on which an inductive programming method is applied for the purpose of deriving a decision tree for decision-making. The process of this transform is referred to as a bio-mapping. The constructed decision trees select about 10 out of 50,000 k-mers. This small set of selected k-mers is regarded as a set of decisive templates. By doing so, non-orthogonal decision trees are constructed using the selected templates and the prediction accuracy is significantly improved.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yang</LastName>
<ForeName>Zheng Rong</ForeName>
<Initials>ZR</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, Exeter University, United Kingdom. z.r.yang@exeter.ac.uk</Affiliation>
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Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.

Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.

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Descripteurs français

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Abstract

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