Alignment using genetic programming with causal trees for identification of protein functions.
Identifieur interne : 002108 ( PubMed/Curation ); précédent : 002107; suivant : 002109Alignment 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 ] ; 2006.
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.
DOI: 10.1016/j.na.2005.09.048
PubMed: 32288048
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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.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32288048</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0362-546X</ISSN><JournalIssue CitedMedium="Print"><Volume>65</Volume><Issue>5</Issue><PubDate><Year>2006</Year><Month>Sep</Month><Day>01</Day></PubDate></JournalIssue><Title>Nonlinear analysis, theory, methods & applications</Title><ISOAbbreviation>Nonlinear Anal Theory Methods Appl</ISOAbbreviation></Journal><ArticleTitle>Alignment using genetic programming with causal trees for identification of protein functions.</ArticleTitle><Pagination><MedlinePgn>1070-1093</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.na.2005.09.048</ELocationID><Abstract><AbstractText>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.</AbstractText><CopyrightInformation>Copyright © 2005 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hung</LastName><ForeName>Chun-Min</ForeName><Initials>CM</Initials><AffiliationInfo><Affiliation>Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Yueh-Min</ForeName><Initials>YM</Initials><AffiliationInfo><Affiliation>Department of Engineering Science, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chang</LastName><ForeName>Ming-Shi</ForeName><Initials>MS</Initials><AffiliationInfo><Affiliation>Department of Biochemistry, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan, ROC.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2005</Year><Month>11</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Nonlinear Anal Theory Methods Appl</MedlineTA><NlmUniqueID>101491193</NlmUniqueID><ISSNLinking>0362-546X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bioinformatics protein databases</Keyword><Keyword MajorTopicYN="N">Evolutionary computing and genetic algorithms</Keyword><Keyword MajorTopicYN="N">Invariants</Keyword><Keyword MajorTopicYN="N">Moments</Keyword><Keyword MajorTopicYN="N">Splines</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>4</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2006</Year><Month>9</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2006</Year><Month>9</Month><Day>1</Day><Hour>0</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32288048</ArticleId><ArticleId IdType="doi">10.1016/j.na.2005.09.048</ArticleId><ArticleId IdType="pii">S0362-546X(05)00902-8</ArticleId><ArticleId IdType="pmc">PMC7117053</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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