Bioinformatics, AI, and Machine Learning in Microbial Drug Development
- Nyhet
Häftad, Engelska, 2025
Av Vagish Dwibedi, Nancy George, Santosh Kumar Rath, Swapnil Kajale, Israel) Dwibedi, Vagish, PhD (Visiting Scientist, Institute of Soil, Water and Environmental Sciences, Agriculture Research Organization (ARO) - Volcani Institute, India) George, Nancy (Chandigarh University, India) Rath, Santosh Kumar, PhD (DIT University, Israel) Kajale, Swapnil, PhD (Postdoctoral Scientist, Agriculture Research Organization (ARO) - Volcani Institute, Rishon Lezhion
2 239 kr
Beställningsvara. Skickas inom 11-20 vardagar
Fri frakt för medlemmar vid köp för minst 249 kr.Bioinformatics, AI, and Machine Learning in Microbial Drug Development provides a comprehensive framework for integrating diverse fields like microbiology, bioinformatics, artificial intelligence, and machine learning, allowing readers to navigate complex interdisciplinary challenges effectively. This title offers an in-depth exploration of how these technologies are seamlessly integrated into pharmaceutical microbiology. The book, divided into 4 parts and 19 chapters, provides cutting-edge insights, practical guidance, and case studies. It emphasizes the importance of staying current with technological advancements, understanding ethical and regulatory issues, and optimizing drug production, making it an invaluable resource for industry professionals.
- Explores the intersection of microbes and cutting-edge technology, revolutionizing drug discovery
- Delves into the possibilities to using bioinformatics to create microbial diversity, optimize drug production, and navigate ethical considerations
- Serves as a response to the growing demand for a holistic guide that integrates diverse fields such as microbiology, bioinformatics, artificial intelligence, and machine learning in the context of drug discovery and production
Produktinformation
- Utgivningsdatum2025-10-31
- Mått191 x 235 x undefined mm
- Vikt450 g
- FormatHäftad
- SpråkEngelska
- Antal sidor462
- FörlagElsevier Science
- ISBN9780443330322