| Autor: |
Cornelia Walther, Michael C. Martinetz, Anja Friedrich, Anne-Luise Tscheließnig, Martin Voigtmann, Alexander Jung, Cécile Brocard, Erich Bluhmki, Jens Smiatek |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Chemical Engineering, Vol 5 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-2718 |
| DOI: |
10.3389/fceng.2023.1227620 |
| Popis: |
We present explainable machine learning approaches for gaining deeper insights into the solubilization processes of inclusion bodies. The machine learning model with the highest prediction accuracy for the protein yield is further evaluated with regard to Shapley additive explanation (SHAP) values in terms of feature importance studies. Our results highlight an inverse fractional relationship between the protein yield and total protein concentration. Further correlations can also be observed for the dominant influences of the urea concentration and the underlying pH values. All findings are used to develop an analytical expression that is in reasonable agreement with experimental data. The resulting master curve highlights the benefits of explainable machine learning approaches for the detailed understanding of certain biopharmaceutical manufacturing steps. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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