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000912083 1001_ $$0P:(DE-Juel1)178687$$aHelleckes, Laura M.$$b0$$eCorresponding author$$ufzj
000912083 245__ $$aMachine learning in bioprocess development: from promise to practice
000912083 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2023
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000912083 520__ $$aFostered 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.
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000912083 7001_ $$0P:(DE-Juel1)165723$$aHemmerich, Johannes$$b1
000912083 7001_ $$0P:(DE-Juel1)129076$$aWiechert, Wolfgang$$b2$$ufzj
000912083 7001_ $$0P:(DE-Juel1)129081$$avon Lieres, Eric$$b3$$ufzj
000912083 7001_ $$0P:(DE-Juel1)143612$$aGrünberger, Alexander$$b4
000912083 773__ $$0PERI:(DE-600)2011002-9$$a10.1016/j.tibtech.2022.10.010$$gp. S0167779922002815$$n6$$pS0167779922002815$$tTrends in biotechnology$$v41$$x0167-7799$$y2023
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