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A Biologist’s Guide to Artificial Intelligence
Building the foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences
- 1st Edition - February 29, 2024
- Editor: Ambreen Hamadani
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 0 1 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 0 0 - 3
A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides… Read more
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Request a sales quoteA Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future.
This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms.
- Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning
- Equips with new data mining strategies an easy interface into the world of Artificial Intelligence
- Enables researchers to enhance their own sphere of researching Artificial Intelligence
Scholars, Researchers and Scientists working in the diverse areas of life Sciences, biological sciences and computer science, Undergrad and grad students pursuing their degrees in all areas of Life Sciences, industry sector and for trained professional working in this interdisciplinary sector
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1. Exploring artificial intelligence through a biologist's lens
- Introduction
- Machine learning algorithms—the foundations of AI
- Integrating AI with biological science
- Research challenges
- Conclusion
- Chapter 2. The synergy of AI and biology: A transformative partnership
- Introduction
- The transformative power of AI in biology
- The need for AI in biology
- Some applications
- Conclusion
- Chapter 3. Understanding life and evolution using AI
- Introduction
- AI algorithms and techniques
- Significance of AI in biology
- Conclusion
- Chapter 4. Decoding life: Genetics, bioinformatics, and artificial intelligence
- Introduction
- Genetics: Bioinformatics and artificial intelligence interface
- Bioinformatics: a boon for new-age genetics research
- Artificial intelligence in biological research
- AI and ML in plant breeding
- Using AI to study biochemical phenotype
- How does AI aid crop improvement efforts by changing the breeding paradigm?
- Machine learning for biochemical phenotypes
- Machine learning for genomic prediction
- Potential applications of AI and ML in classical and modern plant breeding
- Application of AI in phenomics
- Application of ML in image processing
- Research challenges
- Conclusion
- Chapter 5. AI in healthcare: Pioneering innovations for a healthier tomorrow
- Introduction
- Technological advancement
- How is AI used in healthcare?
- Applications of artificial intelligence in healthcare
- Conclusion
- Chapter 6. Reimagining occupational health and safety in the era of AI
- Introduction
- Understanding the application of AI/ML in workplace safety through vision algorithms
- Workplace exposure assessment of toxic gases using AI techniques
- Workplace exposure assessment of hazardous chemicals using AI techniques
- AI for diagnostic and prevention of occupational lung diseases
- NLP utility for workplace health education and awareness
- Chapter 7. From data to insights: Leveraging machine learning for diabetes management
- Introduction
- Understanding data collection and preprocessing of diabetes-related data
- Machine learning models for diabetes risk prediction
- Predictive modeling for blood glucose monitoring
- Ethical considerations in machine learning for diabetes
- Conclusion
- Chapter 8. Smiles 2.0: The AI dentistry frontier
- Introduction
- Applications of AI in dentistry
- Ethical considerations
- Future scope
- Conclusion
- Chapter 9. Applications and impact of artificial intelligence in veterinary sciences
- Introduction
- Big data in veterinary sciences
- AI in diagnoses
- AI for disease prediction and surveillance
- Veterinary precision medicine
- Robots in veterinary sciences
- AI and the future of veterinary medicine
- Conclusion
- Abbreviations
- Chapter 10. Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation
- Introduction
- Need for AI in precision agriculture
- Application of AI in precision agriculture
- Benefits of precision agriculture using AI
- Challenges and considerations
- Conclusion
- Abbreviations
- Chapter 11. Artificial intelligence in animal farms for management and breeding
- Introduction
- AI and big data in livestock farms
- Identification of animals
- Animal monitoring
- Disease detection and prevention
- Precision nutrition and feed management
- Automation for precision farming
- Genetic improvement and breeding
- Decision support systems
- Improving animal production using AI
- Conclusion
- Chapter 12. Food manufacturing, processing, storage, and marketing using artificial intelligence
- Introduction
- Challenges of AI in food industry
- Future directions of AI in food industry
- Ethical considerations, data privacy concerns, and potential biases
- Recommendation for future research
- Chapter 13. Use of AI in conservation and for understanding climate change
- Introduction
- Ecological modeling
- Biodiversity monitoring and conservation
- Climate change
- Use of AI in smart farming through the Internet of Things
- Conclusion
- Chapter 14. Artificial intelligence in marine biology
- Introduction
- Marine biology, a quick overview
- Big data and marine biology
- Artificial intelligence in marine science
- Challenges and future directions
- Conclusion
- Chapter 15. Advances in robotics for biological sciences
- Introduction
- Principles and features of robotics
- Advancements and contributions—A review
- The foreseeable future
- Robot uprising, is it possible?
- Challenges
- Approval and authentication
- Conclusion
- Chapter 16. Robotics and computer vision for health, food security, and environment
- Introduction
- Conclusion
- Chapter 17. Artificial intelligence in classrooms: How artificial intelligence can aid in teaching biology
- Introduction
- AI educational tools
- Criticisms of AI educational tools
- Conclusion
- Abbreviations
- Chapter 18. Ethical issues around artificial intelligence
- Overview of artificial intelligence
- Machine learning
- Some ethical issues around artificial intelligence
- Global efforts to mitigate the challenges of ethical issues around AI
- Ethical implications of AI-powered surveillance
- Challenges of explainability in AI systems
- AI automation and its effects on employment
- Ethical concerns surrounding AI-powered autonomous weapons
- AI-enabled manipulation techniques and their impacts
- Ethical decision-making and values
- Conclusion
- Abbreviations
- Chapter 19. A meshwork of artificial intelligence and biology: The future of science
- Introduction
- Big data in biology and the role of AI
- AI for rapid breakthroughs in biology
- The promise of AI in biology
- Alignment of AI with trends in biological sciences
- Science fiction and AI
- The future of the meshwork
- Research challenges involved
- Cautious steps forward
- Conclusion
- Index
- No. of pages: 368
- Language: English
- Edition: 1
- Published: February 29, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443240010
- eBook ISBN: 9780443240003
AH
Ambreen Hamadani
Dr Ambreen Hamdani (BVSc & AH, MVSc, PhD) is currently a Post Doctoral Fellow at the National Institute of Technology, Srinagar. Her PhD work was centred around the use of Artificial Intelligence for the prediction of genetic merit in Animals. She has published more than a hundred papers and a book. She has also won more than 30 international and national awards including (DST, Lockheed Martin, Tata Trust) India Innovation Growth Program 2019, National level elocution and writing contests, debating contests, the young scientists award for best scientific presentation etc. She has been granted a copyright and has published a patent as well. She has also been selected for a post-doctoral fellowship at the University of Wisconsin-Madison, USA.
Affiliations and expertise
Post Doctoral Fellow, National Institute of Technology, Srinagar, India