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Current Bioinformatics, Volume 7, Number 4, 2012 ISSN: 1574-8936, eISSN: 2212-392X
Precision Public Health is a new and rapidly evolving field, that examines the application of new technologies to public health policy and practice. It draws on a broad range of disciplines including genomics, spatial data, data linkage, epidemiology, health informatics, big data, predictive analytics and communications. The hope is that these new technologies will strengthen preventive health, improve access to health care, and reach disadvantaged populations in all areas of the world. But what are the downsides and what are the risks, and how can we ensure the benefits flow to those population groups most in need, rather than simply to those individuals who can afford to pay? This is the first collection of theoretical frameworks, analyses of empirical data, and case studies to be assembled on this topic, published to stimulate debate and promote collaborative work.
Advances in computer science and technology and in biology over the last several years have opened up the possibility for computing to help answer fundamental questions in biology and for biology to help with new approaches to computing. Making the most of the research opportunities at the interface of computing and biology requires the active participation of people from both fields. While past attempts have been made in this direction, circumstances today appear to be much more favorable for progress. To help take advantage of these opportunities, this study was requested of the NRC by the National Science Foundation, the Department of Defense, the National Institutes of Health, and the Department of Energy. The report provides the basis for establishing cross-disciplinary collaboration between biology and computing including an analysis of potential impediments and strategies for overcoming them. The report also presents a wealth of examples that should encourage students in the biological sciences to look for ways to enable them to be more effective users of computing in their studies.
The nature of biomedical research has been evolving in recent years. Technological advances that make it easier to study the vast complexity of biological systems have led to the initiation of projects with a larger scale and scope. In many cases, these large-scale analyses may be the most efficient and effective way to extract functional information from complex biological systems. Large-Scale Biomedical Science: Exploring Strategies for Research looks at the role of these new large-scale projects in the biomedical sciences. Though written by the National Academies' Cancer Policy Board, this book addresses implications of large-scale science extending far beyond cancer research. It also identifies obstacles to the implementation of these projects, and makes recommendations to improve the process. The ultimate goal of biomedical research is to advance knowledge and provide useful innovations to society. Determining the best and most efficient method for accomplishing that goal, however, is a continuing and evolving challenge. The recommendations presented in Large-Scale Biomedical Science are intended to facilitate a more open, inclusive, and accountable approach to large-scale biomedical research, which in turn will maximize progress in understanding and controlling human disease.
Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations
"This book provides a wide compendium of references to topics in the field of the databases systems and applications"--Provided by publisher.
MATLAB® in bioscience and biotechnology presents an introductory Matlab course oriented towards various collaborative areas of biotechnology and bioscience. It concentrates on Matlab fundamentals and gives examples of its application to a wide range of current bioengineering problems in computational biology, molecular biology, bio-kinetics, biomedicine, bioinformatics, and biotechnology. In the last decade Matlab has been presented to students as the first computer program they learn. Consequently, many non-programmer students, engineers and scientists have come to regard it as user-friendly and highly convenient in solving their specific problems. Numerous books are available on programming in Matlab for engineers in general, irrespective of their specialization, or for those specializing in some specific area, but none have been designed especially for such a wide, interdisciplinary, and topical area as bioengineering. Thus, in this book, Matlab is presented with examples and applications to various school-level and advanced bioengineering problems - from growing populations of microorganisms and population dynamics, reaction kinetics and reagent concentrations, predator-prey models, mass-transfer and flow problems, to sequence analysis and sequence statistics. - This is the first book intended as a manual introducing biologists and other biotechnology engineers to work with Matlab - It is suitable for beginners and inexperienced users; however, applications of Matlab to advanced problems such as the Monte Carlo method, curve fitting, and reliable machine diagnostics make the book relevant to university teachers as well - The book is different in that it assumes a modest mathematical background for the reader and introduces the mathematical or technical concepts with a somewhat traditional approach; Matlab is then used as a tool for subsequent computer solution
This second, extensively revised and updated edition of Health Informatics: An Overview includes new topics which address contemporary issues and challenges and shift the focus on the health problem space towards a computer perspective.
This book highlights the latest breakthrough developments in bioinformatics. It presents a series of timely, in-depth reviews, drug clinical trial studies, biodiversity informatics and thematic issues. In addition, it includes insightful reviews on advances in computational molecular/structural biology, which address areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Innovations in these fields have direct impacts on key issues related to healthcare, medicine, genetic disorders, the development of agricultural products, renewable energy, and environmental protection. Written by respected leaders in the field and covering a wide range of topics involving the integration of biology with computer and information science, the book offers an ideal basis for teaching at the undergraduate and graduate levels. It can also be used for self-instruction by research investigators interested in applying bioinformatics-based analytical methods and information technologists working with academic and industrial laboratories.