Machine Learning and Big Data-enabled Biotechnology

Hal S. Alper

ISBN 10: 3527354743 / ISBN 13: 9783527354740

Published by Wiley-VCH Gmbh Mär 2026, 2026

About this Item

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.

Seller Inventory # 9783527354740

Bibliographic Details

• Title: Machine Learning and Big Data-enabled Biotechnology
• Publisher: Wiley-VCH Gmbh Mär 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 3527354743
• ISBN 13: 9783527354740
• Binding: Buch
• Condition: Neu
• Dimensions: 247W x 174H x 28D millimeters 247 millimeters width by 174 millimeters height by 28 millimeters depth
• Weight: 958 grams

About this title

Synopsis

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.

About the Author

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.