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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},
}