Machine Learning and Big Data-enabled Biotechnology

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.

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Produktdetails:

• Herausgeber:in: Hal S. Alper
• Erscheinungsdatum: 04.03.2026
• Sprache: Englisch
• Seitenumfang: 432 Seiten
• ISBN: 9783527354740