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 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. 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 Introduction Marine biology, a quick overview Big data and marine biology Data generation Data preprocessing Data quality and integrity Artificial intelligence in marine science Species identification Monitoring of marine life Mammal monitoring Coral reef monitoring Marine robots Ocean monitoring and prediction Conservation and marine protected areas Climate change impact assessment Mariculture and AI Challenges and future directions Data availability Model robustness in diverse marine environments Future directions Conclusion References 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 References 16 . Robotics and computer vision for health, food security, and environment Introduction Robotics Applications Healthcare Surgical-assisted intervention Robotic device for radiotherapy Nursing assistant in patient transport Reduction in the spread of infectious diseases and pandemics Food security Environment Computer vision Applications Healthcare Analysis of medical image Machine learning algorithms for medical images Food security Strategies for early disease detection and classification Fruit quantity and quality detection Environment Conclusion References 17 . Artificial intelligence in classrooms: How artificial intelligence can aid in teaching biology Introduction AI educational tools Disability tools Biology-specific disability tool: ForAlexa Intelligent tutors and teachable agents Biology-specific intelligent tutors or agents: Inquiry ITS and Betty's brain Chatbots Biology-specific chatbot: Unnamed chatbot using LINE Personalized learning systems or environments Biology-specific personalized learning systems or environments: Brightspace LeaP Visualizations and virtual reality Biology-specific visualizations and virtual learning environments: BioVR Criticisms of AI educational tools Conclusion Abbreviations References 18 . Ethical issues around artificial intelligence Overview of artificial intelligence Machine learning Deep learning Classification of AI based on their capabilities and functionalities Some ethical issues around artificial intelligence Impact of biased AI decisions on marginalized groups Mitigating bias in AI training data and algorithms Global efforts to mitigate the challenges of ethical issues around AI Ethical implications of AI-powered surveillance Challenges of explainability in AI systems Auditing and regulation of AI algorithms AI automation and its effects on employment Ethical concerns surrounding AI-powered autonomous weapons Promoting responsible use of AI in military applications AI-enabled manipulation techniques and their impacts Combating disinformation in the age of AI Ethical decision-making and values Conclusion Abbreviations References 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 AI handling big data about tiny things Making human lives better The world around us 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 References Contributors Copyright A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences Index A B C D E F G H I J K L M N O P Q R S T U V W X 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