Machine Learning and Big Data-enabled Biotechnology
Language: English
Published by Wiley-VCH Gmbh Mär 2026, 2026
- Hardcover
- New
Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermanyRheinberg-Buch Andreas Meier eK
AbeBooks seller since November 17, 2008
Condition: New
£ 140.50
Quantity: 2 available
Add to basketItem description from seller
Neuware -The book discusses how Machine Learning and Big Data is and can be used in biotechnology for a wide breath of topics. It is separated into three main parts, with the first covering DNA and ranging from synthetic biology part design (such as promoters) to predictions from genome sequences . The second part concerns proteins, with topics ranging from structure and design tools to pathway discovery / retrobiosynthesis , while the last part covers whole cells and ranges from Machine Learning approaches for gene expression to Machine Learning predictions of phenotype and bioreactor performance 432 pp. Englisch.
Seller Inventory # 9783527354740
- Title
- Machine Learning and Big Data-enabled Biotechnology
- Author
- Hal S. Alper
- Publisher
- Wiley-VCH Gmbh Mär 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3527354743
- ISBN 13
- 9783527354740
- Item weight
- 958 grams
- Dimensions
- 247x174x28 mm
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.
Rheinberg-Buch Andreas Meier eK
Bergisch Gladbach, Germany
AbeBooks seller since November 17, 2008
Shipping rates from Germany to U.S.A.
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