Dr. Reyed M. Reyed works at the edge of contemporary biomedical science, where conventional models increasingly fail to explain complex disease behavior. His research spans Microbial Biotechnology, Clinical Microbiology, and AI-driven Precision Nutrition, but is unified by a central objective: transforming how biological complexity is understood, modeled, and acted upon. Rather than approaching disease as a fixed diagnostic entity, his work frames it as a dynamic system emerging from continuous interactions between host biology, microbiome networks, and environmental pressures. This perspective is operationalized through multi-omics integration and artificial intelligence, enabling the transition from fragmented interpretation to structured, computable biological insight. He is the originator of the Polybiome Systems Medicine framework―a systems-level model that reconceptualizes health and disease as emergent properties of interconnected biological processes. The framework extends beyond integration by providing a functional architecture through which genomics, metabolomics, nutrigenomics, and microbial ecology can be analyzed as a unified system. Building on this foundation, Dr. Reyed developed the Reyed Precision Convergence Framework for Antimicrobial Resistance (RPCF-AMR), a multi-scale approach designed to advance the predictive modeling and precision intervention of antimicrobial resistance. By integrating AI-driven platforms (including NexDi), nanobiotechnology, and coordinated system architectures (SCF–DNPS), the framework establishes a continuous pipeline linking molecular dynamics, computational prediction, and targeted therapeutic design. Within this structure, antimicrobial resistance is treated not as a late-stage clinical outcome, but as a system that can be modeled, anticipated, and strategically influenced. This shifts the focus from reactive management toward predictive, data-informed intervention, while remaining aligned with and extending the One Health paradigm into a computationally actionable domain. Across these contributions, Dr. Reyed’s work is defined by a consistent direction: moving biomedical science from descriptive complexity toward engineered, measurable, and scalable systems of precision health. In parallel with his research, he serves as an editor, scientific mentor, and international collaborator, contributing to the development of interdisciplinary frameworks that bridge computational innovation with real-world biomedical application.
Pranav Kumar Prabhakar is currently working as Professor and Head at Research and Development Cell, Lovely Professional University, Punjab, India. He is one among the World’s Top 2% Scientists (list published by Stanford University, USA, 2021 and 2022). He has completed his Ph.D. in Biotechnology from IIT Madras. His main area of research interest focuses on elucidating molecular mechanism and strategies for oral insulin delivery and mimics the signaling pathways in metabolic disorders (diabetes) with natural products. He is Member of Royal Society of Chemistry, and Asia-Pacific Chemical, Biological & Environmental Engineering Society. He is also serving as Editorial Board Member and Reviewer for many reputed national and international journals. He received honors and is Recipient of travel grant toward attending ATTD 2009 in Greece from Indian Institute of Technology Madras & Council for Scientific and Industrial Research (CSIR) and approved by Department of Science and Technology (DST). He has published 75+ research articles in journals, authored 3 books, and 14 chapters. He also delivered 9 oral and poster presentations in scientific meeting.
A. K. Haghi is a retired Professor and a globally prolific scholar with over 1,500 publications, including books, chapters, and refereed journal papers, accumulating more than 4,300 citations with an h-index of 35. He serves as an Honorary Research Associate at the Chemistry Centre, University of Coimbra (Portugal) and the Universidad Autónoma de Coahuila (Mexico). With over 30 years of expertise in engineering and chemoinformatics, Prof. Haghi is the founder and former Editor-in-Chief of the International Journal of Chemoinformatics and Chemical Engineering. He holds advanced degrees from the University of North Carolina (USA) and the Université de Technologie de Compiègne (France), with a Ph.D. in Engineering Sciences from the Université de Franche-Comté (France). Within this volume, his vast experience in computational modeling and material sciences provides a foundational academic authority to the Bio-Digital Polybiomics paradigm, bridging decades of engineering excellence with next-generation diagnostic innovations.
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Paperback. Etat : new. Paperback. Health and disease are increasingly understood through the lens of interconnected biological systems shaped by host genetics, microbial ecology, nutritional inputs, metabolic regulation, and environmental exposures. Advances in multi-omics technologies and artificial intelligence have accelerated the ability to examine these systems at high resolution, revealing previously obscured patterns that govern immune equilibrium, metabolic stability, and long-term disease susceptibility. As global burdens of cancer, diabetes, inflammatory disorders, and environmental toxicity continue to rise, there is an urgent need for integrative frameworks that move beyond reductionist models and toward predictive, preventive, and personalized approaches. Advancing Personalized Medicine with AI-Driven Microbiome and Nutrigenomics: Innovations and Future Perspectives presents a comprehensive systems-level exploration anchored in the polybiome framework-a model that unifies microbial fermentomics, nutrigenomic modulation, immunometabolic regulation, and environmental interactions. The volume introduces NexDi, an AI-enabled diabetes reclassification system based on extensive cohort data, illustrating how multi-omics integration can predict metabolic instability and b-cell vulnerability before clinical manifestation. Additional chapters extend this framework to oncological microenvironments, immune-microbial signaling networks, precision nutrition algorithms, engineered microbial consortia, and ethical governance in AI-driven healthcare. This reference is designed for researchers, clinicians, data scientists, and policymakers seeking rigorous methodologies and translational insights to advance personalized medicine, chronic disease prevention, and biointelligent health systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798337331225
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Taschenbuch. Etat : Neu. Advancing Personalized Medicine With AI-Driven Microbiome and Nutrigenomics | Innovations and Future Perspectives | Reyed M. Reyed (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337331225 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 134781751
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Health and disease are increasingly understood through the lens of interconnected biological systems shaped by host genetics, microbial ecology, nutritional inputs, metabolic regulation, and environmental exposures. Advances in multi-omics technologies and artificial intelligence have accelerated the ability to examine these systems at high resolution, revealing previously obscured patterns that govern immune equilibrium, metabolic stability, and long-term disease susceptibility. As global burdens of cancer, diabetes, inflammatory disorders, and environmental toxicity continue to rise, there is an urgent need for integrative frameworks that move beyond reductionist models and toward predictive, preventive, and personalized approaches. Advancing Personalized Medicine with AI-Driven Microbiome and Nutrigenomics: Innovations and Future Perspectives presents a comprehensive systems-level exploration anchored in the polybiome framework-a model that unifies microbial fermentomics, nutrigenomic modulation, immunometabolic regulation, and environmental interactions. The volume introduces NexDi, an AI-enabled diabetes reclassification system based on extensive cohort data, illustrating how multi-omics integration can predict metabolic instability and ß-cell vulnerability before clinical manifestation. Additional chapters extend this framework to oncological microenvironments, immune-microbial signaling networks, precision nutrition algorithms, engineered microbial consortia, and ethical governance in AI-driven healthcare. This reference is designed for researchers, clinicians, data scientists, and policymakers seeking rigorous methodologies and translational insights to advance personalized medicine, chronic disease prevention, and biointelligent health systems. N° de réf. du vendeur 9798337331225
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