The DOI matches its Crossref record; 240 of the 240 references it cites were found in a registry or an index.
Machine learning for metabolic engineering: A review
Open accessAbstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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Keasling, J., Garcia Martin, H., Lee, T. S., Mukhopadhyay, A., Singer, S. W., & Sundstrom, E. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00577-w · Cited by 294
Self-citationMachine learning for biochemical engineering: A review
Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B. A., Del Rio-Chanona, E. A., & Zhang, D. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.108054 · Cited by 188
Application of Artificial Intelligence in Lung Cancer
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138
Bioprocessing 4.0 in Biomanufacturing: Paving the Way for Sustainable Bioeconomy
Pandey, K., Pandey, M., Kumar, V., Aggarwal, U., & Singhal, B. (2024) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-023-00206-y · Cited by 58
Helmy, M., Elhalis, H., Liu, Y., Chow, Y., & Selvarajoo, K. (2023) · Advances in Nutrition · doi:10.1016/j.advnut.2022.11.002 · Cited by 57
Metadata
- Authors
- Lawson, C. E., Martí, J. M., Radivojevic, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., Singer, S. W., Mukhopadhyay, A., Tanjore, D., Dunn, J. G., & Garcia Martin, H.
- Venue
- Metabolic Engineering
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 63
- Pages
- 34-60
- ISSN
- 1096-7176 (ISSN-L)
- OpenAlex
- W3105098767
- Cited by
- 313
- References
- 240
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Lawson, C. E., Martí, J. M., Radivojevic, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., Singer, S. W., Mukhopadhyay, A., Tanjore, D., Dunn, J. G., & Garcia Martin, H. (2021). Machine learning for metabolic engineering: A review. Metabolic Engineering. https://doi.org/10.1016/j.ymben.2020.10.005
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