Bairong Shen – författare

This book introduces readers to essential methods and applications in translational biomedical informatics, which include biomedical big data, cloud computing and algorithms for understanding omics data, imaging data, electronic health records and public health data. The storage, retrieval, mining and knowledge discovery of biomedical big data will be among the key challenges for future translational research. The paradigm for precision medicine and healthcare needs to integratively analyze not only the data at the same level – e.g. different omics data at the molecular level – but also data from different levels – the molecular, cellular, tissue, clinical and public health level. This book discusses the following major aspects: the structure of cross-level data; clinical patient information and its shareability; and standardization and privacy. It offers a valuable guide for all biologists, biomedical informaticians and clinicians with an interest in Precision Medicine Informatics.

This book is about the transformation of the biomedical information to smart healthcare, the chapters are designed to discuss the health associated factors such as genetics, lifestyle, nutrition and environmental factors. The interactions of these factors and the informatics for the analyses of their effects on health are also covered. The era of aging is approaching and the P4 (predictive, preventive, personalized and participatory) medicine paradigm is becoming practical and reality. According to the Kondratiev''s long wave theory, IT (information technology) and health will be the next technological revolution for the new economic cycle. This book is written for biomedical informatics scientists, clinicians, health practitioners and researchers, etc.

The book addresses the interplay of healthcare and big data management. Thanks to major advances in big data technologies and precision medicine, healthcare is now becoming the new frontier for both scientific research and economic development. This volume covers a range of aspects, including: big data management for healthcare; physiological and gut microbiota – data collection and analysis; big data standardization and ontology; and personal data privacy and systems level modeling in the healthcare context. The book offers a valuable resource for biomedical informaticians, clinicians, health practitioners and researchers alike.

This book introduces the translational informatics applied to most aspects of virus infection, including tracking of virus origin, detection and prevention of infection, drug discovery, and vaccine design as well as smart city-level monitoring and controlling of the virus epidemic by government. It covers the informatics for data mining and modelling at molecular, tissue/organ, individual, and population levels. The informatics for immunological mechanisms and the personalized prediction and treatment of infected patients are also summarized. The perspectives on the application of artificial intelligence to the prevention of virus outbreaks are also given. This book will be helpful to readers who are interested in prevention of virus infection, biomedical informatics, and artificial intelligence in medicine and healthcare.

The book provides readers the informatics and data-driven models for the discovery of personalized excise prescriptions applied to different cases. Overdiagnosis or over-treatment often happened since the complex interaction among the lifestyle, genetic, and environmental factors. Sports and exercise are reported efficient to prevent or reduce the risk of diseases, but the interactions between sports/exercise and disease are personalized and complex. Translational informatics is a powerful paradigm and it promotes the transfer of big data, knowledge and models to the precision application of sports to prevent diseases. Sports and exercise may have different effects on diverse diseases including cancers, neurodegenerative disease, and cardiovascular diseases, etc. This book covers many modern informatics models such as ontologies, knowledge graphs, blockchain, participatory medicine, semantic artificial intelligence, big data modeling, and so on. It also describes the challenges for the sports and exercise medical data sharing and standardization, the privacy protection of data as well as the integration of data from genomic level to physiological phenotype level. This book will be helpful to the readers who are interesting in sports and exercise medicine, healthcare, big data modeling, artificial intelligence in medicine and healthcare.