Machine Learning and Big Data-enabled Biotechnology

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 Biotechnologydiscusses 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 inMachine Learning and Big Data-enabled Biotechnologyinclude:
Deep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequencesDe novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approachesMetabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell modelsAutomated function and learning in biofoundries and strain designsMachine learning predictions of phenotype and bioreactor performance
Machine Learning and Big Data-enabled Biotechnologyearns 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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