Machine Learning Big Data Enabled (21 results)

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

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

Condition: New. pp. 432.

Buch. Condition: 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.

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

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 432 | Sprache: Englisch | Produktart: Bücher | 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.

Buch. Condition: Neu. Neuware -Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fieldsWiley-VCH GmbH, Boschstraße 12, 69469 Weinheim 432 pp. Englisch.

Buch. Condition: 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.

Buch. Condition: 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.

Hardcover. Condition: new. Hardcover. 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 sequencesDe novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approachesMetabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell modelsAutomated function and learning in biofoundries and strain designsMachine 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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 sequencesDe novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approachesMetabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell modelsAutomated function and learning in biofoundries and strain designsMachine 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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 sequencesDe novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approachesMetabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell modelsAutomated function and learning in biofoundries and strain designsMachine 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.