Artificial Intelligence in Immunoengineering

Methods, Models, and Translational Applications

Häftad, Engelska, 2027

2 289 kr

Kommande

Artificial Intelligence in Immunoengineering: Methods, Models, and Translational Applications examines how AI reshapes immunoengineering to design immune-modulating biomaterials, imaging systems, and diagnostic tools. It presents an integrated view of data-driven approaches that accelerate discovery and translation in biomedicine. Convergence is critical to the success of immunoengineering, and drawing on fields outside of its largely biomaterials-based beginnings, to areas such as biomedical data science, computational medicine, and public health, quickening its progress and broadening its impact. This book surveys biological data from single-cell to spatial omics and proteogenomics, and offers practical machine learning frameworks, generative and mechanistic models, and multimodal integration strategies. It addresses bench-to-bedside translation, validation pipelines, and regulatory considerations for AI-enabled immunoengineering tools. The book highlights the rapid progress of multidisciplinary research within immunotherapy and immunoengineering. Looking forward, the prospects of immunoengineering appear promising, with further advancements in disease prevention, diagnostics, and treatment on the horizon. Graduate students, academic researchers, clinician-scientists, and computational biologists gain standardized workflows, mechanism-aware modeling, and reproducible pipelines that advance immunotherapies and diagnostics from concept to clinical impact.

Standardizes AI workflows for immune data, providing templated pipelines, pitfalls, and validation checklists
Presents mechanism-aware modeling that combines predictive ML with causal and dynamical approaches tailored to immune biology
Offers translation and regulatory guidance, including study design, validation, documentation, and deployment considerations

Produktinformation

Utgivningsdatum2027-01-01
Mått191 x 235 x undefined mm
FormatHäftad
SpråkEngelska
Antal sidor440
FörlagElsevier Science
ISBN9780443516573

Tillhör följande kategorier

Biologi inom Naturvetenskap och teknik

Dr. Steven Zanganeh is an Assistant Professor at the New York Institute of Technology, USA. He earned his PhD in Biomedical Engineering from the University of Connecticut, USA, in 2014. He completed his postdoctoral training at Stanford University, USA, and Memorial Sloan Kettering Cancer Center in New York, USA.Dr. Thomas J. Webster is a pioneering professor in biomedical engineering, recognized for his leadership in academia and industry. His research focuses on applying nanotechnology to develop advanced materials for medical use, significantly improving healthcare outcomes. Dr. Webster has held prominent faculty positions in top U.S. and international universities, chaired departments, and directed international centers in biomaterials. He has also founded numerous companies translating his research into FDA-approved medical products. Widely published and a sought-after speaker, his work has been featured in major media outlets and science museums. Dr. Webster is renowned for organizing global conferences and symposia on nanomedicine and biomaterials, fostering interdisciplinary collaboration. He is the recipient of numerous prestigious awards and fellowships from leading scientific and medical organizations, demonstrating his impact and innovation in biomaterials, nanomedicine, and biomedical engineering.

1. Artificial Intelligence in Immunoengineering: Scope and Opportunities2. Immune Data Landscape: From Single-Cell to Spatial and Proteogenomics3. AI in Tissue Engineering and Regenerative Immunology4. Biomaterials, Nanomedicine, and AI-Driven Immunomodulation5. Machine Learning Frameworks for Immune and Bioengineering Data6. Generative and Mechanistic Models in Bioengineering and Immunity7. Multimodal Fusion: Integrating Imaging, Omics, and Engineered Models8. AI in Immunoimaging and Digital Pathology9. From Bench to Bedside: Validation, Deployment, and Regulation10. Future Directions: Digital Twins, Systems Bioengineering, and Responsible AI