Machine Learning and Big Data-enabled Biotechnology
eBook - ePub

Machine Learning and Big Data-enabled Biotechnology

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Machine Learning and Big Data-enabled Biotechnology

About this book

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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Yes, you can access Machine Learning and Big Data-enabled Biotechnology by Hal S. Alper in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Table of Contents
  3. Title Page
  4. Copyright
  5. Preface
  6. 1 From Genome to Actionable Insights in Biotechnology
  7. 2 Automated Approaches for the Development of Genome-Scale Metabolic Network Models
  8. 3 Machine-Guided Approaches for Synthetic Biology Part Design
  9. 4 Machine Learning for Sequence-to-Function Approaches
  10. 5 Prediction of Enzyme Functions by Artificial Intelligence
  11. 6 Design of Biochemical Pathways via AI/ML-Enabled Retrobiosynthesis
  12. 7 Machine Learning to Accelerate the Discovery of Therapeutic Peptides
  13. 8 Machine Learning Approaches for High-Throughput Microbial Identification/Culturing
  14. 9 Generative AI for Knowledge Mining of Synthetic Biology and Bioprocess Engineering Literature
  15. 10 Metabolomics: Big Data Approaches
  16. 11 Strain Engineering, Flux Design, and Metabolic Production Using Big Data: Ongoing Advances and Opportunities
  17. 12 Next-Generation Metabolic Flux Analysis Using Machine Learning
  18. 13 Streamlining the Design-Build-Test-Learn Process in Automated Biofoundries
  19. 14 Machine Learning-Enhanced Hybrid Modeling for Phenotype Prediction and Bioreactor Optimization
  20. Index
  21. End User License Agreement