Alper H Machine Learning and Big Data-enabled Biotechnology 2026 | 7.35 MB

Title: Machine Learning and Big Data-enabled Biotechnology
Author: Hal S. Alper;

Description:
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:

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• 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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