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

Buch. Zustand: Neu. 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.…

Buch. Zustand: Neu. 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.…

Taschenbuch. Zustand: Neu. Systems Metabolic Engineering | Methods and Protocols | Hal S. Alper | Taschenbuch | xii | Englisch | 2017 | Humana | EAN 9781493962921 | Verantwortliche Person für die EU: Humana Press in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[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 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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Paperback. Zustand: Brand New. reprint edition. 486 pages. 9.25x6.10x1.15 inches. In Stock.

Zustand: New. 2026. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 488 | Sprache: Englisch | Produktart: Bücher | With the ultimate goal of systematically and robustly defining the specific perturbations necessary to alter a cellular phenotype, systems metabolic engineering has the potential to lead to a complete cell model capable of simulating cell and metabolic function as well as predicting phenotypic response to changes in media, gene knockouts/overexpressions, or the incorporation of heterologous pathways.  In Systems Metabolic Engineering: Methods and Protocols, experts in the field describe the methodologies and approaches in the area of systems metabolic engineering and provide a step-by-step guide for their implementation. Four major tenants of this approach are addressed, including modeling and simulation, multiplexed genome engineering, ¿omics technologies, and large data-set incorporation and synthesis, all elucidated through the use of model host organisms.  Written in the highly successful Methods in Molecular Biology¿ series format, chapters include introductions on their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.  Comprehensive and cutting-edge, Systems Metabolic Engineering: Methods and Protocols serves as an ideal guide for metabolic engineers, molecular biologists, and microbiologists aiming to implement the most recent approaches available in the field.…

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.…

Softcover. Zustand: gut. 2017. Systems Metabolic Engineering In englischer Sprache. pages.