Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers introduces revolutionary techniques that merge artificial intelligence with graph-based methods to uncover complex biological networks. Through detailed examples and case studies, the book provides researchers and practitioners the essential tools to analyze molecular interactions, identify key biomarkers, and hasten the discovery of novel therapeutics. Chapters delve into the sophisticated interplay between advanced AI techniques and graph models, specially designed to decode the intricacies of biological systems. By utilizing cutting-edge AI algorithms, readers can explore complex biological networks, forecast molecular interactions, and pinpoint new drug targets with exceptional precision.
1. Chapter 1 Introduction to AI-powered graph models in biological sciences - p. 1 2. Chapter 2 Fundamentals of graph theory and biological networks - p. 31 3. Chapter 3 Leveraging natural language processing and graph models for sarcasm detection in text: Applications in health informatics and biomedical research - p. 63 4. Chapter 4 Graph-driven machine learning frameworks for survival prediction in colon cancer genomics - p. 83 5. Chapter 5 Network modeling in drug-disease systems prediction: A review - p. 105 6. Chapter 6 Exploring gene expression changes in Drosophila melanogaster with RNA-sequencing data analysis - p. 121 7. Chapter 7 Biomedical research tools, applications, and case studies - p. 145 8. Chapter 8 Integrating graph neural networks for predictive modeling in complex biological systems - p. 169 9. Chapter 9 Ethical considerations in AI-powered healthcare and data security - p. 185 10. Chapter 10 AI-enhanced drug discovery and repurposing using graph models - p. 199 11. Chapter 11 Intelligent genetic algorithm-based computational neuroscience model to detect epileptichese - p. 219 12. Index - p. 241
Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers introduces revolutionary techniques that merge artificial intelligence with graph-based methods to uncover complex biological networks. Through detailed examples and case studies, the book provides researchers and practitioners the essential tools to analyze molecular interactions, identify key biomarkers, and hasten the discovery of novel therapeutics. Chapters delve into the sophisticated interplay between advanced AI techniques and graph models, specially designed to decode the intricacies of biological systems. By utilizing cutting-edge AI algorithms, readers can explore complex biological networks, forecast molecular interactions, and pinpoint new drug targets with exceptional precision.
1. Chapter 1 Introduction to AI-powered graph models in biological sciences - p. 1 2. Chapter 2 Fundamentals of graph theory and biological networks - p. 31 3. Chapter 3 Leveraging natural language processing and graph models for sarcasm detection in text: Applications in health informatics and biomedical research - p. 63 4. Chapter 4 Graph-driven machine learning frameworks for survival prediction in colon cancer genomics - p. 83 5. Chapter 5 Network modeling in drug-disease systems prediction: A review - p. 105 6. Chapter 6 Exploring gene expression changes in Drosophila melanogaster with RNA-sequencing data analysis - p. 121 7. Chapter 7 Biomedical research tools, applications, and case studies - p. 145 8. Chapter 8 Integrating graph neural networks for predictive modeling in complex biological systems - p. 169 9. Chapter 9 Ethical considerations in AI-powered healthcare and data security - p. 185 10. Chapter 10 AI-enhanced drug discovery and repurposing using graph models - p. 199 11. Chapter 11 Intelligent genetic algorithm-based computational neuroscience model to detect epileptichese - p. 219 12. Index - p. 241
