Machine Learning and Big Data-Enabled Biotechnology (Hardcover) (ISBN-13: 9783527354740)

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.

Author: Hal S. Alper
ISBN-10: 3527354743
ISBN-13: 9783527354740
Publisher: Wiley-Vch
Language: English
Published: 05/11/2026
Pages: 432
Format: Hardcover
Weight: 1.95lbs
Size: 9.61h x 6.69w x 0.94d