ISBN: 9783527354740 - machine learning and big data-enabled biotechnology (advanced biotechnology) (9 Ergebnisse)

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Hardcover. Zustand: Brand New. 432 pages. 6.69x0.59x9.61 inches. In Stock.

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Buch. Zustand: Neu. Machine Learning and Big Data-enabled Biotechnology | Hal S. Alper | Buch | 432 S. | Englisch | 2026 | Wiley-VCH GmbH | EAN 9783527354740 | Verantwortliche Person für die EU: Wiley-VCH GmbH, Boschstr. 12, 69469 Weinheim, product-safety[at]wiley[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. Neuware - Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fieldsMachine 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 performanceMachine 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.…

Buch. Zustand: Neu. Neuware -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.Wiley-VCH GmbH, Boschstraße 12, 69469 Weinheim 432 pp. Englisch.…

Buch. Zustand: Neu. Neuware -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. 432 pp. Englisch.…