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Machine Learning and Big Data-enabled Biotechnology : Advanced Biotechnology - Hal S. Alper

Machine Learning and Big Data-enabled Biotechnology

By: Hal S. Alper (Editor)

Hardcover | 4 February 2026

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Hardcover


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Available: 4th February 2026

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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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