A biologist's guide to artificial intelligence : building the foundations of artificial intelligence and machine learning for achieving advancements in life sciences /
A 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 f...
| Corporate Author: | |
|---|---|
| Other Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
London, United Kingdom :
Academic Press,
2024.
|
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Front Cover
- A Biologist's Guide to Artificial Intelligence
- A Biologist's Guide to Artificial Intelligence: Building the foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences
- Copyright
- Contents
- Contributors
- 1
- Exploring artificial intelligence through a biologist's lens
- Introduction
- Machine learning algorithms-the foundations of AI
- Integrating AI with biological science
- Logistic regression
- Support vector machine
- Gradient boosting
- Clustering
- Genetic algorithm
- Fuzzy logic
- Neural network/multilayer perceptron
- Convolutional neural network
- Recurrent neural network
- Graph convolutional network
- Research challenges
- Conclusion
- References
- 2
- The synergy of AI and biology: A transformative partnership
- Introduction
- The transformative power of AI in biology
- Machine learning
- Deep learning
- Natural language processing
- Reinforcement learning
- Data integration and fusion
- Genetic algorithms
- The need for AI in biology
- Some applications
- Healthcare
- Precision medicine and personalized treatment
- Genomics and genetic research
- Image analysis and medical imaging
- Biological network analysis
- Ecology and conservation
- Synthetic biology and bio-engineering
- Robot-assisted surgery using artificial intelligence
- The promise of AI
- Limitations of using AI in biology
- Conclusion
- References
- 3
- Understanding life and evolution using AI
- Introduction
- AI algorithms and techniques
- Machine learning
- Deep learning
- Natural language processing
- Image analysis and computer vision
- Genetic algorithms
- Bayesian networks
- Data integration and pattern recognition
- 3D modeling and reconstruction
- Significance of AI in biology
- AI in genomics research
- AI and the Human Genome Project.
- AI in ecology
- AI in evolutionary biology
- Conclusion
- References
- 4
- Decoding life: Genetics, bioinformatics, and artificial intelligence
- Introduction
- Genetics: Bioinformatics and artificial intelligence interface
- Bioinformatics: a boon for new-age genetics research
- Genome analysis
- Nucleotide sequencing and analysis
- Gene prediction
- Genome annotation
- Transcriptome analysis
- Protein analysis
- 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
- Assessments of biotic and abiotic stress
- Artificial intelligence in crop genomics
- Prediction of functional genomic regions
- Application of AI in phenomics
- Application of ML in image processing
- Research challenges
- Conclusion
- References
- 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
- Wearable sensors
- Radiology
- Medical image analysis
- Electronic health records and analytics
- Precision medicine
- Smart internet of medical things and diagnostic analysis
- Conclusion
- References
- 6
- Reimagining occupational health and safety in the era of AI
- Introduction
- NLP in occupational health and safety
- RBES in occupational health and safety
- ML for occupational health and safety
- DL for occupational health and safety
- 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
- Generative AI models
- AI for diagnostic and prevention of occupational lung diseases
- NLP utility for workplace health education and awareness
- References
- 7
- From data to insights: Leveraging machine learning for diabetes management
- Introduction
- Overview of diabetes and its management challenges
- Type 1 (juvenile diabetes)
- Type 2 (diabetes mellitus)
- Gestational diabetes
- Role of machine learning in diabetes management
- Understanding data collection and preprocessing of diabetes-related data
- Collection and preprocessing of diabetes-related data
- Data collection
- Data preprocessing
- Standard datasets for diabetes research
- Machine learning models for diabetes risk prediction
- Logistic regression model
- Decision tree model
- Random forest
- Support vector machine
- K-means clustering
- Neural networks
- Deep learning models
- Predictive modeling for blood glucose monitoring
- Ensemble models for blood glucose monitoring
- Continuous glucose monitoring and machine learning
- Ethical considerations in machine learning for diabetes
- Privacy and security in diabetes data handling
- Addressing bias and fairness in machine learning models
- Explainability and interpretability of ML-based diabetes solutions
- Conclusion
- References
- 8
- Smiles 2.0: The AI dentistry frontier
- Introduction
- Applications of AI in dentistry
- Operative dentistry
- Endodontics
- Endodontic diagnosis
- Treatment planning
- Regenerative endodontics
- Periodontics
- Orthodontics
- Oral and maxillofacial pathology
- Prosthodontics
- Oral and maxillofacial surgery
- Public health dentistry
- Forensic odontology
- Patient management
- Ethical considerations
- Future scope
- Conclusion
- References.
- 9
- Applications and impact of artificial intelligence in veterinary sciences
- Introduction
- Big data in veterinary sciences
- AI in diagnoses
- Imaging
- AI for disease prediction and surveillance
- Veterinary precision medicine
- Robots in veterinary sciences
- AI and the future of veterinary medicine
- Robots as pets
- Conclusion
- Abbreviations
- References
- 10
- Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation
- Introduction
- Understanding precision agriculture
- Need for AI in precision agriculture
- Application of AI in precision agriculture
- Data collection and analysis
- Crop monitoring and management
- Weed and pest control
- Irrigation and soil management
- Autonomous farming
- Benefits of precision agriculture using AI
- Increased productivity and yield
- Resource efficiency
- Cost reduction and economic viability
- Environmental sustainability
- Challenges and considerations
- Data quality and integration
- Infrastructure and connectivity
- Technical expertise and training
- Ethical and regulatory considerations
- Conclusion
- Abbreviations
- References
- 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
- References
- 12
- Food manufacturing, processing, storage, and marketing using artificial intelligence
- Introduction
- Food manufacturing
- Application of AI in food manufacturing
- Benefits of AI in food manufacturing
- Implementation of AI in food manufacturing
- Food processing.
- Application of AI in food processing
- AI potential in food processing
- Food storage using AI
- The role of AI in food storage practices
- Application of AI for food storage
- Food marketing
- The use of AI in food marketing strategies
- Implementation of AI in marketing
- 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
- References
- 13
- Use of AI in conservation and for understanding climate change
- Introduction
- Ecological modeling
- History
- Ecological modeling for river water quality
- Lake modeling
- Forest modeling
- Integrated models
- Section summary
- Biodiversity monitoring and conservation
- Climate change
- A brief history of the origin of AI usage in climate monitoring and change
- Present usage of AI in climate change research
- Harnessing AI for energy efficiency
- Harnessing AI for environment monitoring, planning, and resource management
- Deforestation prediction and monitoring
- Ecosystem restoration planning
- Water resource management
- Land use planning
- AI in renewable energy
- Harnessing AI to counter-forest fires, flooding, and desertification
- Flooding
- Wildfires
- Desertification
- Future of AI in climate change and challenges
- Section summary
- Use of AI in smart farming through the Internet of Things
- Introduction to AI in smart farming through the Internet of Things
- Using IoT for precision agriculture
- Crop management
- Crop productivity
- Weed detection
- Precision fertilizers
- Pest detection and management
- The main application domains of IoT in agriculture
- Open issues and challenges in smart farming
- Section summary
- Conclusion
- Author contributions
- References
- 14
- Artificial intelligence in marine biology.