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:
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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Dr. Hal S. Alper is the Cockrell Family Regents Chair in Engineering #1 at The University of Texas at Austin in the McKetta Department of Chemical Engineering. His research focuses on applying and extending the approaches of metabolic engineering, synthetic biology, systems biology, and protein engineering.
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Librería: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Alemania
Buch. Condición: 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. Nº de ref. del artículo: 9783527354740
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Buch. Condición: 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. Nº de ref. del artículo: 9783527354740
Cantidad disponible: 2 disponibles
Librería: Wegmann1855, Zwiesel, Alemania
Buch. Condición: Neu. Neuware -Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields. Nº de ref. del artículo: 9783527354740
Cantidad disponible: 2 disponibles