Alignment using genetic programming with causal trees for identification of protein functions
Identifieur interne : 001397 ( Pmc/Checkpoint ); précédent : 001396; suivant : 001398Alignment using genetic programming with causal trees for identification of protein functions
Auteurs : Chun-Min Hung [République populaire de Chine] ; Yueh-Min Huang [République populaire de Chine] ; Ming-Shi Chang [République populaire de Chine]Source :
- Nonlinear Analysis, Theory, Methods & Applications [ 0362-546X ] ; 2005.
Abstract
A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.
Url:
DOI: 10.1016/j.na.2005.09.048
PubMed: NONE
PubMed Central: 7117053
Affiliations:
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wicri:level="1"><nlm:aff id="aff1">Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC</nlm:aff><country xml:lang="fr" wicri:curation="lc">République populaire de Chine</country><wicri:regionArea>Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan</wicri:regionArea><wicri:noRegion>Taiwan</wicri:noRegion></affiliation></author><author><name sortKey="Huang, Yueh Min" sort="Huang, Yueh Min" uniqKey="Huang Y" first="Yueh-Min" last="Huang">Yueh-Min Huang</name><affiliation wicri:level="1"><nlm:aff id="aff1">Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC</nlm:aff><country xml:lang="fr" wicri:curation="lc">République populaire de Chine</country><wicri:regionArea>Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan</wicri:regionArea><wicri:noRegion>Taiwan</wicri:noRegion></affiliation></author><author><name sortKey="Chang, Ming Shi" sort="Chang, Ming Shi" uniqKey="Chang M" first="Ming-Shi" last="Chang">Ming-Shi Chang</name><affiliation wicri:level="1"><nlm:aff id="aff2">Department of Biochemistry, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC</nlm:aff><country xml:lang="fr" wicri:curation="lc">République populaire de Chine</country><wicri:regionArea>Department of Biochemistry, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan</wicri:regionArea><wicri:noRegion>Taiwan</wicri:noRegion></affiliation></author></analytic><series><title level="j">Nonlinear Analysis, Theory, Methods & Applications</title><idno type="ISSN">0362-546X</idno><idno type="eISSN">0362-546X</idno><imprint><date when="2005">2005</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Delcoigne, A" uniqKey="Delcoigne A">A. Delcoigne</name></author><author><name sortKey="Hansen, P" uniqKey="Hansen P">P. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Nonlinear Anal Theory Methods Appl</journal-id><journal-id journal-id-type="iso-abbrev">Nonlinear Anal Theory Methods Appl</journal-id><journal-title-group><journal-title>Nonlinear Analysis, Theory, Methods & Applications</journal-title></journal-title-group><issn pub-type="ppub">0362-546X</issn><issn pub-type="epub">0362-546X</issn><publisher><publisher-name>Elsevier Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmc">7117053</article-id><article-id pub-id-type="publisher-id">S0362-546X(05)00902-8</article-id><article-id pub-id-type="doi">10.1016/j.na.2005.09.048</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Alignment using genetic programming with causal trees for identification of protein functions</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hung</surname><given-names>Chun-Min</given-names></name><email>goodmans@giga.net.tw</email><xref rid="aff1" ref-type="aff">a</xref></contrib><contrib contrib-type="author"><name><surname>Huang</surname><given-names>Yueh-Min</given-names></name><email>huang@mail.ncku.edu.tw</email><xref rid="aff1" ref-type="aff">a</xref><xref rid="cor1" ref-type="corresp">⁎</xref></contrib><contrib contrib-type="author"><name><surname>Chang</surname><given-names>Ming-Shi</given-names></name><email>mschang@mail.ncku.edu.tw</email><xref rid="aff2" ref-type="aff">b</xref></contrib></contrib-group><aff id="aff1"><label>a</label>Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC</aff><aff id="aff2"><label>b</label>Department of Biochemistry, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC</aff><author-notes><corresp id="cor1"><label>⁎</label>Corresponding author. Tel.: +886 93 993 1111; fax: +886 6276 6549. <email>huang@mail.ncku.edu.tw</email></corresp></author-notes><pub-date pub-type="pmc-release"><day>28</day><month>11</month><year>2005</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><day>1</day><month>9</month><year>2006</year></pub-date><pub-date pub-type="epub"><day>28</day><month>11</month><year>2005</year></pub-date><volume>65</volume><issue>5</issue><fpage>1070</fpage><lpage>1093</lpage><permissions><copyright-statement>Copyright © 2005 Elsevier Ltd. All rights reserved.</copyright-statement><copyright-year>2005</copyright-year><copyright-holder>Elsevier Ltd</copyright-holder><license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p></license></permissions><abstract><p>A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.</p></abstract><kwd-group><title>Keywords</title><kwd>Bioinformatics protein databases</kwd><kwd>Evolutionary computing and genetic algorithms</kwd><kwd>Splines</kwd><kwd>Invariants</kwd><kwd>Moments</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Hung, Chun Min" sort="Hung, Chun Min" uniqKey="Hung C" first="Chun-Min" last="Hung">Chun-Min Hung</name></noRegion><name sortKey="Chang, Ming Shi" sort="Chang, Ming Shi" uniqKey="Chang M" first="Ming-Shi" last="Chang">Ming-Shi Chang</name><name sortKey="Huang, Yueh Min" sort="Huang, Yueh Min" uniqKey="Huang Y" first="Yueh-Min" last="Huang">Yueh-Min Huang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= Alignment using genetic programming with causal trees for identification of protein functions
}}
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HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Checkpoint/RBID.i -Sk "pubmed:NONE" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Checkpoint/biblio.hfd \
| NlmPubMed2Wicri -a SrasV1
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