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• W3209916183 abstract "Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues." @default.
• W3209916183 created "2021-11-08" @default.
• W3209916183 creator A5026523298 @default.
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• W3209916183 date "2021-12-01" @default.
• W3209916183 modified "2023-09-26" @default.
• W3209916183 title "Improving the performance of machine learning models for biotechnology: The quest for deus ex machina" @default.
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• W3209916183 cites W2044525257 @default.
• W3209916183 cites W2058501095 @default.
• W3209916183 cites W2061042286 @default.
• W3209916183 cites W2078355959 @default.
• W3209916183 cites W2091198609 @default.
• W3209916183 cites W2096008230 @default.
• W3209916183 cites W2103795997 @default.
• W3209916183 cites W2110118110 @default.
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• W3209916183 cites W2153908768 @default.
• W3209916183 cites W2158469604 @default.
• W3209916183 cites W2166659140 @default.
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• W3209916183 cites W2346907959 @default.
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• W3209916183 cites W2577168473 @default.
• W3209916183 cites W2582666522 @default.
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• W3209916183 doi "https://doi.org/10.1016/j.biotechadv.2021.107858" @default.
• W3209916183 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34695560" @default.
• W3209916183 hasPublicationYear "2021" @default.
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