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001     54190
005     20190625112052.0
024 7 _ |2 pmid
|a pmid:17005536
024 7 _ |2 DOI
|a 10.1093/bioinformatics/btl489
024 7 _ |2 WOS
|a WOS:000242715200007
024 7 _ |a altmetric:3216061
|2 altmetric
037 _ _ |a PreJuSER-54190
041 _ _ |a eng
082 _ _ |a 004
084 _ _ |2 WoS
|a Biochemical Research Methods
084 _ _ |2 WoS
|a Biotechnology & Applied Microbiology
084 _ _ |2 WoS
|a Computer Science, Interdisciplinary Applications
084 _ _ |2 WoS
|a Mathematical & Computational Biology
084 _ _ |2 WoS
|a Statistics & Probability
100 1 _ |a Zimmermann, O.
|b 0
|u FZJ
|0 P:(DE-Juel1)132307
245 _ _ |a Support Vector Machines for Prediction of Dihedral Angle Regions
260 _ _ |a Oxford
|b Oxford University Press
|c 2006
300 _ _ |a 3009
336 7 _ |a Journal Article
|0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|0 0
|2 EndNote
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a article
|2 DRIVER
440 _ 0 |a Bioinformatics
|x 1367-4803
|0 13881
|v 22
500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information.We propose a multi-step support vector machine (SVM) procedure, dihedral prediction (DHPRED), to predict the dihedral angle state of residues from sequence. Trained on 20,000 residues our approach leads to dihedral region predictions, that in regions without alpha helices or beta sheets is higher than those from secondary structure prediction programs.DHPRED has been implemented as a web service, which academic researchers can access from our webpage http://www.fz-juelich.de/nic/cbb
536 _ _ |a Scientific Computing
|c P41
|2 G:(DE-HGF)
|0 G:(DE-Juel1)FUEK411
|x 0
588 _ _ |a Dataset connected to Web of Science, Pubmed
650 _ 2 |2 MeSH
|a Algorithms
650 _ 2 |2 MeSH
|a Amino Acid Sequence
650 _ 2 |2 MeSH
|a Artificial Intelligence
650 _ 2 |2 MeSH
|a Computer Simulation
650 _ 2 |2 MeSH
|a Models, Chemical
650 _ 2 |2 MeSH
|a Models, Molecular
650 _ 2 |2 MeSH
|a Molecular Sequence Data
650 _ 2 |2 MeSH
|a Pattern Recognition, Automated: methods
650 _ 2 |2 MeSH
|a Protein Structure, Secondary
650 _ 2 |2 MeSH
|a Proteins: chemistry
650 _ 2 |2 MeSH
|a Proteins: ultrastructure
650 _ 2 |2 MeSH
|a Sequence Alignment: methods
650 _ 2 |2 MeSH
|a Sequence Analysis, Protein: methods
650 _ 7 |0 0
|2 NLM Chemicals
|a Proteins
650 _ 7 |a J
|2 WoSType
700 1 _ |a Hansmann, U. H. E.
|b 1
|u FZJ
|0 P:(DE-Juel1)VDB46160
773 _ _ |a 10.1093/bioinformatics/btl489
|g Vol. 22, p. 3009
|p 3009
|q 22<3009
|0 PERI:(DE-600)1468345-3
|t Bioinformatics
|v 22
|y 2006
|x 1367-4803
856 7 _ |u http://dx.doi.org/10.1093/bioinformatics/btl489
909 C O |o oai:juser.fz-juelich.de:54190
|p VDB
913 1 _ |k P41
|v Scientific Computing
|l Supercomputing
|b Schlüsseltechnologien
|0 G:(DE-Juel1)FUEK411
|x 0
914 1 _ |y 2006
915 _ _ |0 StatID:(DE-HGF)0010
|a JCR/ISI refereed
920 1 _ |k NIC
|l John von Neumann - Institut für Computing
|g NIC
|0 I:(DE-Juel1)NIC-20090406
|x 0
970 _ _ |a VDB:(DE-Juel1)84957
980 _ _ |a VDB
980 _ _ |a ConvertedRecord
980 _ _ |a journal
980 _ _ |a I:(DE-Juel1)NIC-20090406
980 _ _ |a UNRESTRICTED

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