Remote homology detection based on oligomer distances.
Identifieur interne : 000448 ( Ncbi/Checkpoint ); précédent : 000447; suivant : 000449Remote 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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pubmed:16837522Le document en format XML
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<term>Données de séquences moléculaires</term>
<term>Intelligence artificielle</term>
<term>Protéines ()</term>
<term>Reconnaissance automatique des formes</term>
<term>Similitude de séquences d'acides aminés</term>
<term>Séquence d'acides aminés</term>
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<term>Dimerization</term>
<term>Molecular Sequence Data</term>
<term>Pattern Recognition, Automated</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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