Decoding Cancer: Precision Oncology through AI, Machine Learning, and Multi-Omics Analytics offers an integrated perspective on how computational technologies are transforming cancer research and care. It bridges cancer biology with advanced data-driven approaches to enable precision oncology. The book covers fundamentals of cancer biology alongside applications of artificial intelligence, machine learning, and big data analytics in biomarker discovery, early detection, and personalized treatment. Chapters explore multi-omics integration, predictive modeling, radiomics, surgical robotics, and ethical considerations, supported by diagrams, case studies, and annotated workflows. For academics and professionals, this resource provides actionable insights into cutting-edge tools and methodologies, helping readers understand how computational oncology accelerates translational research and clinical decision-making. It serves as a comprehensive guide for students, researchers, and clinicians navigating the future of data-driven cancer care.Integrates cancer biology with AI, ML, and big data for precision oncologyExplains real-world applications through case studies, diagrams, and annotated workflowsAddresses ethical, regulatory, and future trends in computational cancer care

Decoding Cancer: Precision Oncology through AI, Machine Learning, and Multi-Omics Analytics offers an integrated perspective on how computational technologies are transforming cancer research and care. It bridges cancer biology with advanced data-driven approaches to enable precision oncology. The book covers fundamentals of cancer biology alongside applications of artificial intelligence, machine learning, and big data analytics in biomarker discovery, early detection, and personalized treatment. Chapters explore multi-omics integration, predictive modeling, radiomics, surgical robotics, and ethical considerations, supported by diagrams, case studies, and annotated workflows. For academics and professionals, this resource provides actionable insights into cutting-edge tools and methodologies, helping readers understand how computational oncology accelerates translational research and clinical decision-making. It serves as a comprehensive guide for students, researchers, and clinicians navigating the future of data-driven cancer care.Integrates cancer biology with AI, ML, and big data for precision oncologyExplains real-world applications through case studies, diagrams, and annotated workflowsAddresses ethical, regulatory, and future trends in computational cancer care