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@ARTICLE{Kiirikki:1025607,
      author       = {Kiirikki, Anne M. and Antila, Hanne S. and Bort, Lara S.
                      and Buslaev, Pavel and Favela-Rosales, Fernando and
                      Ferreira, Tiago Mendes and Fuchs, Patrick F. J. and
                      Garcia-Fandino, Rebeca and Gushchin, Ivan and Kav, Batuhan
                      and Kučerka, Norbert and Kula, Patrik and Kurki, Milla and
                      Kuzmin, Alexander and Lalitha, Anusha and Lolicato, Fabio
                      and Madsen, Jesper J. and Miettinen, Markus S. and Mingham,
                      Cedric and Monticelli, Luca and Nencini, Ricky and
                      Nesterenko, Alexey M. and Piggot, Thomas J. and Piñeiro,
                      Ángel and Reuter, Nathalie and Samantray, Suman and
                      Suárez-Lestón, Fabián and Talandashti, Reza and Ollila,
                      O. H. Samuli},
      title        = {{O}verlay databank unlocks data-driven analyses of
                      biomolecules for all},
      journal      = {Nature Communications},
      volume       = {15},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {FZJ-2024-03001},
      pages        = {1136},
      year         = {2024},
      abstract     = {Tools based on artificial intelligence (AI) are currently
                      revolutionising many fields, yet their applications are
                      often limited by the lack of suitable training data in
                      programmatically accessible format. Here we propose an
                      effective solution to make data scattered in various
                      locations and formats accessible for data-driven and machine
                      learning applications using the overlay databank format. To
                      demonstrate the practical relevance of such approach, we
                      present the NMRlipids Databank—a community-driven,
                      open-for-all database featuring programmatic access to
                      quality-evaluated atom-resolution molecular dynamics
                      simulations of cellular membranes. Cellular membrane lipid
                      composition is implicated in diseases and controls major
                      biological functions, but membranes are difficult to study
                      experimentally due to their intrinsic disorder and complex
                      phase behaviour. While MD simulations have been useful in
                      understanding membrane systems, they require significant
                      computational resources and often suffer from inaccuracies
                      in model parameters. Here, we demonstrate how programmable
                      interface for flexible implementation of data-driven and
                      machine learning applications, and rapid access to
                      simulation data through a graphical user interface, unlock
                      possibilities beyond current MD simulation and experimental
                      studies to understand cellular membranes. The proposed
                      overlay databank concept can be further applied to other
                      biomolecules, as well as in other fields where similar
                      barriers hinder the AI revolution.},
      cin          = {IBI-7},
      ddc          = {500},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5241},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38326316},
      UT           = {WOS:001159313700005},
      doi          = {10.1038/s41467-024-45189-z},
      url          = {https://juser.fz-juelich.de/record/1025607},
}