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@ARTICLE{Taubert:1018573,
      author       = {Taubert, Oskar and von der Lehr, Fabrice and Bazarova,
                      Alina and Faber, Christian and Knechtges, Philipp and Weiel,
                      Marie and Debus, Charlotte and Coquelin, Daniel and
                      Basermann, Achim and Streit, Achim and Kesselheim, Stefan
                      and Götz, Markus and Schug, Alexander},
      title        = {{RNA} contact prediction by data efficient deep learning},
      journal      = {Communications biology},
      volume       = {6},
      number       = {1},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2023-04901},
      pages        = {913},
      year         = {2023},
      abstract     = {On the path to full understanding of the structure-function
                      relationship or even design of RNA, structure prediction
                      would offer an intriguing complement to experimental
                      efforts. Any deep learning on RNA structure, however, is
                      hampered by the sparsity of labeled training data. Utilizing
                      the limited data available, we here focus on predicting
                      spatial adjacencies ("contact maps”) as a proxy for 3D
                      structure. Our model, BARNACLE, combines the utilization of
                      unlabeled data through self-supervised pre-training and
                      efficient use of the sparse labeled data through an XGBoost
                      classifier. BARNACLE shows a considerable improvement over
                      both the established classical baseline and a deep neural
                      network. In order to demonstrate that our approach can be
                      applied to tasks with similar data constraints, we show that
                      our findings generalize to the related setting of accessible
                      surface area prediction.},
      cin          = {JSC},
      ddc          = {570},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511) / HAF - Helmholtz Analytics Framework
                      (ZT-I-0003) / Helmholtz AI Consultant Team FB Information
                      (E54.303.11)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111 /
                      G:(DE-HGF)ZT-I-0003 / G:(DE-Juel-1)E54.303.11},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37674020},
      UT           = {WOS:001060848200001},
      doi          = {10.1038/s42003-023-05244-9},
      url          = {https://juser.fz-juelich.de/record/1018573},
}