Remote homology detection based on oligomer distances.
Identifieur interne : 002099 ( PubMed/Checkpoint ); précédent : 002098; suivant : 002100Remote homology detection based on oligomer distances.
Auteurs : Thomas Lingner [Allemagne] ; Peter MeinickeSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2006.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Proteins.
- methods : Sequence Alignment, Sequence Analysis, Protein.
- Algorithms, Amino Acid Sequence, Artificial Intelligence, Dimerization, Molecular Sequence Data, Pattern Recognition, Automated, Sequence Homology, Amino Acid.
Abstract
Remote homology detection is among the most intensively researched problems in bioinformatics. Currently discriminative approaches, especially kernel-based methods, provide the most accurate results. However, kernel methods also show several drawbacks: in many cases prediction of new sequences is computationally expensive, often kernels lack an interpretable model for analysis of characteristic sequence features, and finally most approaches make use of so-called hyperparameters which complicate the application of methods across different datasets.
DOI: 10.1093/bioinformatics/btl376
PubMed: 16837522
Affiliations:
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Currently discriminative approaches, especially kernel-based methods, provide the most accurate results. However, kernel methods also show several drawbacks: in many cases prediction of new sequences is computationally expensive, often kernels lack an interpretable model for analysis of characteristic sequence features, and finally most approaches make use of so-called hyperparameters which complicate the application of methods across different datasets.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">16837522</PMID><DateCompleted><Year>2006</Year><Month>09</Month><Day>25</Day></DateCompleted><DateRevised><Year>2009</Year><Month>11</Month><Day>04</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>22</Volume><Issue>18</Issue><PubDate><Year>2006</Year><Month>Sep</Month><Day>15</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Remote homology detection based on oligomer distances.</ArticleTitle><Pagination><MedlinePgn>2224-31</MedlinePgn></Pagination><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Remote homology detection is among the most intensively researched problems in bioinformatics. Currently discriminative approaches, especially kernel-based methods, provide the most accurate results. However, kernel methods also show several drawbacks: in many cases prediction of new sequences is computationally expensive, often kernels lack an interpretable model for analysis of characteristic sequence features, and finally most approaches make use of so-called hyperparameters which complicate the application of methods across different datasets.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We introduce a feature vector representation for protein sequences based on distances between short oligomers. The corresponding feature space arises from distance histograms for any possible pair of K-mers. Our distance-based approach shows important advantages in terms of computational speed while on common test data the prediction performance is highly competitive with state-of-the-art methods for protein remote homology detection. Furthermore the learnt model can easily be analyzed in terms of discriminative features and in contrast to other methods our representation does not require any tuning of kernel hyperparameters.</AbstractText><AbstractText Label="AVAILABILITY" NlmCategory="BACKGROUND">Normalized kernel matrices for the experimental setup can be downloaded at www.gobics.de/thomas. Matlab code for computing the kernel matrices is available upon request.</AbstractText><AbstractText Label="CONTACT" NlmCategory="BACKGROUND">thomas@gobics.de, peter@gobics.de.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lingner</LastName><ForeName>Thomas</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstr. 1, 37077 Göttingen, Germany. thomas@gobics.de</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Meinicke</LastName><ForeName>Peter</ForeName><Initials>P</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2006</Year><Month>07</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011506">Proteins</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000595" MajorTopicYN="N">Amino Acid Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001185" MajorTopicYN="N">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019281" MajorTopicYN="N">Dimerization</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008969" MajorTopicYN="N">Molecular Sequence Data</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010363" MajorTopicYN="N">Pattern Recognition, Automated</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011506" MajorTopicYN="N">Proteins</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016415" MajorTopicYN="N">Sequence Alignment</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020539" MajorTopicYN="N">Sequence Analysis, Protein</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017386" MajorTopicYN="Y">Sequence Homology, Amino Acid</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2006</Year><Month>7</Month><Day>14</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2006</Year><Month>9</Month><Day>26</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2006</Year><Month>7</Month><Day>14</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">16837522</ArticleId><ArticleId IdType="pii">btl376</ArticleId><ArticleId IdType="doi">10.1093/bioinformatics/btl376</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Allemagne</li></country><region><li>Basse-Saxe</li></region><settlement><li>Göttingen</li></settlement></list><tree><noCountry><name sortKey="Meinicke, Peter" sort="Meinicke, Peter" uniqKey="Meinicke P" first="Peter" last="Meinicke">Peter Meinicke</name></noCountry><country name="Allemagne"><region name="Basse-Saxe"><name sortKey="Lingner, Thomas" sort="Lingner, Thomas" uniqKey="Lingner T" first="Thomas" last="Lingner">Thomas Lingner</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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