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Many people naturally assume that the claims made for foods and nutritional supplements have the same degree of scientific grounding as those for medication, but that is not always the case. The IOM recommends that the FDA adopt a consistent scientific framework for biomarker evaluation in order to achieve a rigorous and transparent process.
This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.
In the past decade there has been a major sea change in the way disease is diagnosed and investigated due to the advent of high throughput technologies, such as microarrays, lab on a chip, proteomics, genomics, lipomics, metabolomics etc. These advances have enabled the discovery of new and novel markers of disease relating to autoimmune disorders, cancers, endocrine diseases, genetic disorders, sensory damage, intestinal diseases etc. In many instances these developments have gone hand in hand with the discovery of biomarkers elucidated via traditional or conventional methods, such as histopathology or clinical biochemistry. Together with microprocessor-based data analysis, advanced statistics and bioinformatics these markers have been used to identify individuals with active disease or pathology as well as those who are refractory or have distinguishing pathologies. New analytical methods that have been used to identify markers of disease and is suggested that there may be as many as 40 different platforms. Unfortunately techniques and methods have not been readily transferable to other disease states and sometimes diagnosis still relies on single analytes rather than a cohort of markers. There is thus a demand for a comprehensive and focused evidenced-based text and scientific literature that addresses these issues. Hence the formulation of Biomarkers in Disease. The series covers a wide number of areas including for example, nutrition, cancer, endocrinology, cardiology, addictions, immunology, birth defects, genetics and so on. The chapters are written by national or international experts and specialists.
This new edition offers a state-of-the-art and integrative vision of pharmacogenomics by exploring new concepts and practical methodologies focusing on disease treatments, from cancers to cardiovascular and neurodegenerative disorders and more. The collection of these theoretical and experimental approaches facilitates problem-solving by tackling the complexity of personalized drug discovery and development. Written by leading experts in their fields for the highly successful Methods in Molecular Biology series, the book aims to provide across-the-board resources to support the translation of pharmacogenomics into better individualized health care. Authoritative and up-to-date, Pharmacogenomics in Drug Discovery and Development, Third Edition aims to aid researchers in approaching the challenges in pharmacogenomics and personalized medicine with the introduction of these novel ideas and cutting-edge methodologies.
Recent advances in drug discovery have been rapid. The second edition of Bioinformatics and Drug Discovery has been completely updated to include topics that range from new technologies in target identification, genomic analysis, cheminformatics, protein analysis, and network or pathway analysis. Each chapter provides an extended introduction that describes the theory and application of the technology. In the second part of each chapter, detailed procedures related to the use of these technologies and software have been incorporated. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Bioinformatics and Drug Discovery, Second Edition seeks to aid scientists in the further study of the rapidly expanding field of drug discovery.
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.
Translational Medicine: Tools and Techniques provides a standardized path from basic research to the clinic and brings together various policy and practice issues to simplify the broad interdisciplinary field. With discussions from academic and industry leaders at international institutions who have successfully implemented translational medicine techniques and tools in various settings, readers will be guided through implementation strategies relevant to their own needs and institutions. The book also addresses regulatory processes in USA, EU, Japan and China. By providing details on omics sciences techniques, biomarkers, data mining and management approaches, case reports from industry, and tools to assess the value of different technologies and techniques, this book is the first to provide a user-friendly go-to guide for key opinion leaders (KOLs), industry administrators, faculty members, clinicians, researchers, and students interested in translational medicine. - Includes detailed and standardized information about the techniques and tools used in translational medicine - Provides specific industry case scenarios - Explains how to use translational medicine tools and techniques to plan and improve infrastructures and capabilities while reducing cost and optimizing resources
This book will serve as a primer for both laboratory and field scientists who are shaping the emerging field of molecular epidemiology. Molecular epidemiology utilizes the same paradigm as traditional epidemiology but uses biological markers to identify exposure, disease or susceptibility. Schulte and Perera present the epidemiologic methods pertinent to biological markers. The book is also designed to enumerate the considerations necessary for valid field research and provide a resource on the salient and subtle features of biological indicators.
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 collects and reviews, for the first time, a wide range of advances in the area of human aging biomarkers. This accumulated data allows researchers to assess the rate of aging processes in various organs and systems, and to individually monitor the effectiveness of therapies intended to slow aging. In an introductory chapter, the editor defines biomarkers of aging as molecular, cellular and physiological parameters that demonstrate reproducible changes - quantitative or qualitative - with age. The introduction recounts a study which aimed to create a universal model of biological age, whose most predictive parameters were albumin and alkaline phosphatase (indication liver function), glucose (metabolic syndrome), erythrocytes (respiratory function) and urea (renal function). The book goes on to describe DNA methylation, known as the "epigenetic clock," as currently the most comprehensive predictor of total mortality. It is also useful for predicting mortality from cancer and cardiovascular diseases, and for analyzing the effects of lifestyle factors including diet, exercise, and education. Individual contributions draw additional insight from research on genetics and epigenetic aging markers, and immunosenescence and inflammaging markers. A concluding chapter outlines the challenge of integrating of biological and clinical markers of aging. Biomarkers of Human Aging is written for professionals and practitioners engaged in the study of aging, and will be useful to both advanced students and researchers.