AI in Breast Cancer Stem Cells and Proteomics: Precision Oncology Perspectives explores the intersection of artificial intelligence, cancer stem cell biology, and proteomics to address the persistent challenges of relapse, metastasis, and therapy resistance in breast cancer. This book provides a translational framework for precision oncology through advanced computational and molecular approaches. It gives insight into the biology of breast cancer stem cells (BCSCs), their heterogeneity, and the role of proteomics in identifying biomarkers and therapeutic targets. The integration of AI enables predictive modeling, drug resistance profiling, and multi-omics data analysis, offering novel insights into personalized treatment strategies. Academics benefit from a comprehensive, interdisciplinary resource that bridges cancer biology, bioinformatics, and clinical applications.
The book equips researchers, clinicians, and students with case studies, algorithmic tools, and practical workflows to translate proteomic data into actionable medical interventions.
1. Chapter 1 Introduction to breast cancer stem cells and tumor heterogeneity - p. 1 2. Chapter 2 Breast cancer stem cells across subtypes: Biology, challenges, and recent advances - p. 29 3. Chapter 3 Proteomics in breast cancer: Technologies and applications - p. 65 4. Chapter 4 Foundations of artificial intelligence in biomedical research - p. 89 5. Chapter 5 Artificial intelligence for breast cancer stem cell characterization imaging and diagnostics - p. 127 6. Chapter 6 Proteomic biomarkers and artificial intelligence–based predictive models - p. 149 7. Chapter 7 Artificial intelligence for drug resistance and therapy response prediction in breast cancer stem cells - p. 185 8. Chapter 8 Multiomics integration with artificial intelligence: From bench to bedside in breast cancer - p. 205 9. Chapter 9 Interface between breast cancer stem cells and immunotherapy: Implications for personalized medicine with case studies - p. 233 10. Chapter 10 Future directions: Smart oncology and personalized medicine - p. 263 11. Index - p. 301
AI in Breast Cancer Stem Cells and Proteomics: Precision Oncology Perspectives explores the intersection of artificial intelligence, cancer stem cell biology, and proteomics to address the persistent challenges of relapse, metastasis, and therapy resistance in breast cancer. This book provides a translational framework for precision oncology through advanced computational and molecular approaches. It gives insight into the biology of breast cancer stem cells (BCSCs), their heterogeneity, and the role of proteomics in identifying biomarkers and therapeutic targets. The integration of AI enables predictive modeling, drug resistance profiling, and multi-omics data analysis, offering novel insights into personalized treatment strategies. Academics benefit from a comprehensive, interdisciplinary resource that bridges cancer biology, bioinformatics, and clinical applications.
The book equips researchers, clinicians, and students with case studies, algorithmic tools, and practical workflows to translate proteomic data into actionable medical interventions.
1. Chapter 1 Introduction to breast cancer stem cells and tumor heterogeneity - p. 1 2. Chapter 2 Breast cancer stem cells across subtypes: Biology, challenges, and recent advances - p. 29 3. Chapter 3 Proteomics in breast cancer: Technologies and applications - p. 65 4. Chapter 4 Foundations of artificial intelligence in biomedical research - p. 89 5. Chapter 5 Artificial intelligence for breast cancer stem cell characterization imaging and diagnostics - p. 127 6. Chapter 6 Proteomic biomarkers and artificial intelligence–based predictive models - p. 149 7. Chapter 7 Artificial intelligence for drug resistance and therapy response prediction in breast cancer stem cells - p. 185 8. Chapter 8 Multiomics integration with artificial intelligence: From bench to bedside in breast cancer - p. 205 9. Chapter 9 Interface between breast cancer stem cells and immunotherapy: Implications for personalized medicine with case studies - p. 233 10. Chapter 10 Future directions: Smart oncology and personalized medicine - p. 263 11. Index - p. 301
