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@ARTICLE{Helleckes:912083,
      author       = {Helleckes, Laura M. and Hemmerich, Johannes and Wiechert,
                      Wolfgang and von Lieres, Eric and Grünberger, Alexander},
      title        = {{M}achine learning in bioprocess development: from promise
                      to practice},
      journal      = {Trends in biotechnology},
      volume       = {41},
      number       = {6},
      issn         = {0167-7799},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-05310},
      pages        = {S0167779922002815},
      year         = {2023},
      abstract     = {Fostered by novel analytical techniques, digitalization,
                      and automation, modern bioprocess development provides large
                      amounts of heterogeneous experimental data, containing
                      valuable process information. In this context, data-driven
                      methods like machine learning (ML) approaches have great
                      potential to rationally explore large design spaces while
                      exploiting experimental facilities most efficiently. Herein
                      we demonstrate how ML methods have been applied so far in
                      bioprocess development, especially in strain engineering and
                      selection, bioprocess optimization, scale-up, monitoring,
                      and control of bioprocesses. For each topic, we will
                      highlight successful application cases, current challenges,
                      and point out domains that can potentially benefit from
                      technology transfer and further progress in the field of
                      ML.},
      cin          = {IBG-1},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-1-20101118},
      pnm          = {2172 - Utilization of renewable carbon and energy sources
                      and engineering of ecosystem functions (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2172},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36456404},
      UT           = {WOS:001196982900001},
      doi          = {10.1016/j.tibtech.2022.10.010},
      url          = {https://juser.fz-juelich.de/record/912083},
}