Machine Learning and Big Data-enabled Biotechnology (Advanced Biotechnology) - Hardcover

Hardcover

ISBN 10: 3527354743 ISBN 13: 9783527354740

Verlag: Wiley-VCH, 2026

Inhaltsangabe

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?

Über diesen Titel

Über die Autorin bzw. den Autor

Dr. Hal Alper is the Kenneth A. Kobe Professor in Chemical Engineering and Executive Director of the Center for Biomedical Research Support at The University of Texas at Austin. He earned his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology in 2006 and was a postdoctoral research associate at the Whitehead Institute for Biomedical Research from 2006-2008, and at Shire Human Genetic Therapies from 2007-2008. Dr. Alper also serves on the Graduate Studies Committee for the Cell and Molecular Biology Department and the Biochemistry Department. He is currently the Principal Investigator of the Laboratory for Cellular and Metabolic Engineering at The University of Texas at Austin where his lab focuses on metabolic and cellular engineering in the context of biofuel, biochemical, and biopharmaceutical production in an array of model host organisms. His research focuses on applying and extending the approaches of synthetic biology, systems
biology, and protein engineering.

Von der hinteren Coverseite

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.

Details zur Ausgabe

• Verlag: Wiley-VCH
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 3527354743
• ISBN 13: 9783527354740
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 432
• Herausgeber: Alper Hal S.
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland