This title appears in the Scientific Report :
2006
Please use the identifier:
http://dx.doi.org/10.1093/bioinformatics/btl489 in citations.
Support Vector Machines for Prediction of Dihedral Angle Regions
Support Vector Machines for Prediction of Dihedral Angle Regions
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 dist...
Saved in:
Personal Name(s): | Zimmermann, O. |
---|---|
Hansmann, U. H. E. | |
Contributing Institute: |
John von Neumann - Institut für Computing; NIC |
Published in: | Bioinformatics, 22 (2006) S. 3009 |
Imprint: |
Oxford
Oxford University Press
2006
|
Physical Description: |
3009 |
PubMed ID: |
17005536 |
DOI: |
10.1093/bioinformatics/btl489 |
Document Type: |
Journal Article |
Research Program: |
Scientific Computing |
Series Title: |
Bioinformatics
22 |
Subject (ZB): | |
Publikationsportal JuSER |
LEADER | 03977nam a2200745 a 4500 | ||
---|---|---|---|
001 | 54190 | ||
005 | 20190625112052.0 | ||
980 | |a VDB | ||
980 | |a ConvertedRecord | ||
980 | |a journal | ||
980 | |a I:(DE-Juel1)NIC-20090406 | ||
980 | |a UNRESTRICTED | ||
700 | 1 | |a Hansmann, U. H. E. |b 1 |u FZJ |0 P:(DE-Juel1)VDB46160 | |
856 | 7 | |u http://dx.doi.org/10.1093/bioinformatics/btl489 | |
909 | C | O | |o oai:juser.fz-juelich.de:54190 |p VDB |
970 | |a VDB:(DE-Juel1)84957 | ||
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 | ||
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 | |
915 | |0 StatID:(DE-HGF)0010 |a JCR/ISI refereed | ||
914 | 1 | |y 2006 | |
500 | |a Record converted from VDB: 12.11.2012 | ||
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 | ||
300 | |a 3009 | ||
082 | |a 004 | ||
440 | 0 | |a Bioinformatics |x 1367-4803 |0 13881 |v 22 | |
588 | |a Dataset connected to Web of Science, Pubmed | ||
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 | ||
245 | |a Support Vector Machines for Prediction of Dihedral Angle Regions | ||
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 | ||
260 | |a Oxford |b Oxford University Press |c 2006 | ||
100 | 1 | |a Zimmermann, O. |b 0 |u FZJ |0 P:(DE-Juel1)132307 | |
041 | |a eng | ||
913 | 1 | |k P41 |v Scientific Computing |l Supercomputing |b Schlüsseltechnologien |0 G:(DE-Juel1)FUEK411 |x 0 | |
536 | |a Scientific Computing |c P41 |2 G:(DE-HGF) |0 G:(DE-Juel1)FUEK411 |x 0 | ||
336 | |a ARTICLE |2 BibTeX | ||
336 | |a Nanopartikel unedler Metalle (Mg0, Al0, Gd0, Sm0) |0 0 |2 EndNote | ||
336 | |a Output Types/Journal article |2 DataCite | ||
336 | |a Journal Article |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) | ||
336 | |a article |2 DRIVER | ||
336 | |a JOURNAL_ARTICLE |2 ORCID | ||
920 | |k John von Neumann - Institut für Computing; NIC |l John von Neumann - Institut für Computing |g NIC |0 I:(DE-Juel1)NIC-20090406 |x 0 | ||
990 | |a Zimmermann, Olav |b 0 |u FZJ |0 P:(DE-Juel1)132307 | ||
991 | |a Hansmann, U. H. E. |b 1 |u FZJ |0 P:(DE-Juel1)VDB46160 |