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@ARTICLE{Zimmermann:54190,
      author       = {Zimmermann, O. and Hansmann, U. H. E.},
      title        = {{S}upport {V}ector {M}achines for {P}rediction of
                      {D}ihedral {A}ngle {R}egions},
      journal      = {Bioinformatics},
      volume       = {22},
      issn         = {1367-4803},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {PreJuSER-54190},
      pages        = {3009},
      year         = {2006},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {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},
      keywords     = {Algorithms / Amino Acid Sequence / Artificial Intelligence
                      / Computer Simulation / Models, Chemical / Models, Molecular
                      / Molecular Sequence Data / Pattern Recognition, Automated:
                      methods / Protein Structure, Secondary / Proteins: chemistry
                      / Proteins: ultrastructure / Sequence Alignment: methods /
                      Sequence Analysis, Protein: methods / Proteins (NLM
                      Chemicals) / J (WoSType)},
      cin          = {NIC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)NIC-20090406},
      pnm          = {Scientific Computing},
      pid          = {G:(DE-Juel1)FUEK411},
      shelfmark    = {Biochemical Research Methods / Biotechnology $\&$ Applied
                      Microbiology / Computer Science, Interdisciplinary
                      Applications / Mathematical $\&$ Computational Biology /
                      Statistics $\&$ Probability},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:17005536},
      UT           = {WOS:000242715200007},
      doi          = {10.1093/bioinformatics/btl489},
      url          = {https://juser.fz-juelich.de/record/54190},
}