Machine Learning and Big Data-enabled Biotechnology
Language: English
Published by Wiley-VCH GmbH, 2026
- Hardcover
- New

Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
AbeBooks seller since June 11, 1999
Condition: New
US$ 164.67
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Add to basketItem description from seller
Seller Inventory # DB-9783527354740
- Title
- Machine Learning and Big Data-enabled Biotechnology
- Publisher
- Wiley-VCH GmbH
- Publication year
- 2026
- Condition
- New
- Binding
- HRD
- Language
- English
- ISBN 10
- 3527354743
- ISBN 13
- 9783527354740
- Item weight
- 680 grams
Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields
Machine Learning and Big Data-enabled Biotechnology discusses how machine learning and big data can be used in biotechnology for a wide breadth of topics, providing tools essential to support efforts in process control, reactor performance evaluation, and research target identification.
Topics explored in Machine Learning and Big Data-enabled Biotechnology include:
- Deep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequences
- De novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approaches
- Metabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell models
- Automated function and learning in biofoundries and strain designs
- Machine learning predictions of phenotype and bioreactor performance
Machine Learning and Big Data-enabled Biotechnology earns a well-deserved spot on the bookshelves of reaction, process, catalytic, and environmental engineers seeking to explore the vast opportunities presented by rapidly developing technologies.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Hal S. Alper is the Cockrell Family Regents Chair in Engineering #1 at The University of Texas at Austin in the McKetta Department of Chemical Engineering. His research focuses on applying and extending the approaches of metabolic engineering, synthetic biology, systems biology, and protein engineering.
"About the title" may belong to another edition of this title.
PBShop.store UK
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