Bioinformatics Methods In Clinical Research

Integrated bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen. In Bioinformatics Methods in Clinical Research, experts examine the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Chapters discuss statistics, algorithms, automated methods of data retrieval, and experimental consideration in genomics, transcriptomics, proteomics, and metabolomics. Composed in the highly successful Methods in Molecular BiologyTM series format, each chapter contains a brief introduction, provides practical examples illustrating methods, results, and conclusions from data mining strategies wherever possible, and includes a Notes section which shares tips on troubleshooting and avoiding known pitfalls. Informative and ground-breaking, Bioinformatics Methods in Clinical Research establishes a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

Integrated bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen. In Bioinformatics Methods in Clinical Research, experts examine the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Chapters discuss statistics, algorithms, automated methods of data retrieval, and experimental consideration in genomics, transcriptomics, proteomics, and metabolomics. Composed in the highly successful Methods in Molecular BiologyTM series format, each chapter contains a brief introduction, provides practical examples illustrating methods, results, and conclusions from data mining strategies wherever possible, and includes a Notes section which shares tips on troubleshooting and avoiding known pitfalls. Informative and ground-breaking, Bioinformatics Methods in Clinical Research establishes a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion. The intent of the book is to provide an overview of the primary statistical and data analytic issues associated with each of the selected topics, followed by a discussion of approaches for tackling such issues and available software packages for carrying out analyses. While classic topics such as survival data analysis, analysis of diagnostic test data and assessment of measurement reliability are well known and covered in depth by available topic-specific texts, this volume serves a different purpose: it provides a quick introduction to each topic for self-learning, particularly for those who have not done any formal coursework on a given topic but must learn it due to its relevance to their multidisciplinary research. In addition, the chapters on these classic topics will reflect issues particularly relevant to modern clinical trials such as longitudinal designs and new methods for analyzing data from such study designs. The coverage of these topics provides a quick introduction to these important statistical issues and methods for addressing them. As with the classic topics, this part of the volume on modern topics will enable researchers to grasp the statistical methods for addressing these emerging issues underlying modern clinical trials and to apply them to their research studies.

Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.

The purpose of the book is to provide an overview of clinical research (types), activities, and areas where informatics and IT could fit into various activities and business practices. This book will introduce and apply informatics concepts only as they have particular relevance to clinical research settings.

“Methods of Clinical Epidemiology” serves as a text on methods useful to clinical researchers. It provides a clear introduction to the common research methodology specific to clinical research for both students and researchers. This book sets out to fill the gap left by texts that concentrate on public health epidemiology and focuses on what is not covered well in such texts. The four sections cover methods that have not previously been brought together in one text and serves as a second level textbook of clinical epidemiology methodology. This book will be of use to postgraduate students in clinical epidemiology as well as clinical researchers at the start of their careers.

This book elucidates how genetic, biological and medical information can be applied to the development of personalized healthcare, medication and therapies. Focusing on aspects of the development of evidence-based approaches in bioinformatics and computational medicine, including data integration, methodologies, tools and models for clinical and translational medicine, it offers an essential introduction to clinical bioinformatics for clinical researchers and physicians, medical students and teachers, and scientists working with human disease-based omics and bioinformatics. Dr. Xiangdong Wang is a distinguished Professor of Medicine. He is Director of Shanghai Institute of Clinical Bioinformatics, Director of Fudan University Center for Clinical Bioinformatics, Deputy Director of Shanghai Respiratory Research Institute, Director of Biomedical Research Center, Fudan University Zhongshan Hospital, Shanghai, China; Dr. Christian Baumgartner is a Professor of Health Care and Biomedical Engineering at Institute of Health Care Engineering with European Notified Body of Medical Devices, Graz University of Technology, Graz, Austria; Dr. Denis Shields is a Professor of Clinical Bioinformatics at Conway Institute, Belfield, Dublin, Ireland; Dr. Hong-Wen Deng is a Professor at Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, USA; Dr. Jacques S Beckmann is a Professor and Director of Section of Clinical Bioinformatics, Swiss Institute of Bioinformatics, Switzerland.