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@INBOOK{Zimmermann:837815,
      author       = {Zimmermann, Olav},
      title        = {{B}ackbone {D}ihedral {A}ngle {P}rediction},
      volume       = {1484},
      address      = {New York, NY},
      publisher    = {Springer New York},
      reportid     = {FZJ-2017-06604},
      isbn         = {978-1-4939-6404-8 (print)},
      series       = {Methods in Molecular Biology},
      pages        = {65 - 82},
      year         = {2017},
      comment      = {Prediction of Protein Secondary Structure / Zhou, Yaoqi
                      (Editor) ; New York, NY : Springer New York, 2017, Chapter 7
                      ; ISSN: 1064-3745=1940-6029 ; ISBN:
                      978-1-4939-6404-8=978-1-4939-6406-2 ;
                      doi:10.1007/978-1-4939-6406-2},
      booktitle     = {Prediction of Protein Secondary
                       Structure / Zhou, Yaoqi (Editor) ; New
                       York, NY : Springer New York, 2017,
                       Chapter 7 ; ISSN: 1064-3745=1940-6029 ;
                       ISBN:
                       978-1-4939-6404-8=978-1-4939-6406-2 ;
                       doi:10.1007/978-1-4939-6406-2},
      abstract     = {More than two decades of research have enabled dihedral
                      angle predictions at an accuracy that makes them an
                      interesting alternative or supplement to secondary structure
                      prediction that provides detailed local structure
                      information for every residue of a protein. The evolution of
                      dihedral angle prediction methods is closely linked to
                      advancements in machine learning and other relevant
                      technologies. Consequently recent improvements in
                      large-scale training of deep neural networks have led to the
                      best method currently available, which achieves a mean
                      absolute error of 19° for phi, and 30° for psi. This
                      performance opens interesting perspectives for the
                      application of dihedral angle prediction in the comparison,
                      prediction, and design of protein structures.},
      cin          = {JSC},
      ddc          = {570},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511)},
      pid          = {G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)7},
      UT           = {WOS:000400734600008},
      doi          = {10.1007/978-1-4939-6406-2_7},
      url          = {https://juser.fz-juelich.de/record/837815},
}