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| Author | : Guangchuang Yu |
| Publisher | : Frontiers Media SA |
| Total Pages | : 146 |
| Release | : 2022-05-24 |
| Genre | : Science |
| ISBN | : 2889761908 |
| Author | : Robert Hoyt |
| Publisher | : Lulu.com |
| Total Pages | : 260 |
| Release | : 2019-11-25 |
| Genre | : Science |
| ISBN | : 179476173X |
Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises.
| Author | : Gintautas Dzemyda |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 262 |
| Release | : 2012-11-08 |
| Genre | : Mathematics |
| ISBN | : 1441902368 |
This book highlights recent developments in multidimensional data visualization, presenting both new methods and modifications on classic techniques. Throughout the book, various applications of multidimensional data visualization are presented including its uses in social sciences (economy, education, politics, psychology), environmetrics, and medicine (ophthalmology, sport medicine, pharmacology, sleep medicine). The book provides recent research results in optimization-based visualization. Evolutionary algorithms and a two-level optimization method, based on combinatorial optimization and quadratic programming, are analyzed in detail. The performance of these algorithms and the development of parallel versions are discussed. The utilization of new visualization techniques to improve the capabilies of artificial neural networks (self-organizing maps, feed-forward networks) is also discussed. The book includes over 100 detailed images presenting examples of the many different visualization techniques that the book presents. This book is intended for scientists and researchers in any field of study where complex and multidimensional data must be represented visually.
| Author | : Katarína Furmanová |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2024-09-12 |
| Genre | : Mathematics |
| ISBN | : 9783031667886 |
This book provides an overview of the many visualization strategies that have been proposed in recent decades for solving problems within the disciplines of medicine and biology. It also evaluates which visualization techniques applied to various areas of biomedicine have been the most impactful and which challenges can be considered solved using visualization. The topics covered in this book include visualization research in omics, interaction networks and pathways, biological structures, tumor diagnosis and treatment, vasculature, brain, surgery, educational contexts, therapy and rehabilitation, electronic health records, and public health. One chapter is dedicated to general visualization techniques commonly used for biomedical data, such as surface and volume rendering, as well as abstract and illustrative approaches. For each of these areas, the past and present research trends are discussed, highlighting the influential works. Furthermore, the book explains how research is affected by developments in technology, data availability, and domain practice. Individual sections also summarize the typical target users, the nature of the data, and the typical tasks addressed in the given domain. With a uniquely broad scope, the book identifies research trends in biomedical visualization using a manually curated and searchable repository of more than 3,800 publications. The resultant trends are further categorized according to the application field and using natural language processing. The book also utilizes 16 interviews conducted with researchers in the field of biomedical visualization for the purpose of soliciting their opinions regarding the evolution and trends in the domain. The book handles these topics in a concise manner to help readers orient themselves in the already mature and diverse field of biomedical visualization without overwhelming them with technical details. As such, the book can help young researchers to become familiar with past challenges and identify future ones. For more senior researchers, it can serve as an insightful overview of the research field and the direction in which it is heading.
| Author | : Charles D. Hansen |
| Publisher | : Springer |
| Total Pages | : 397 |
| Release | : 2014-09-18 |
| Genre | : Mathematics |
| ISBN | : 1447164970 |
Based on the seminar that took place in Dagstuhl, Germany in June 2011, this contributed volume studies the four important topics within the scientific visualization field: uncertainty visualization, multifield visualization, biomedical visualization and scalable visualization. • Uncertainty visualization deals with uncertain data from simulations or sampled data, uncertainty due to the mathematical processes operating on the data, and uncertainty in the visual representation, • Multifield visualization addresses the need to depict multiple data at individual locations and the combination of multiple datasets, • Biomedical is a vast field with select subtopics addressed from scanning methodologies to structural applications to biological applications, • Scalability in scientific visualization is critical as data grows and computational devices range from hand-held mobile devices to exascale computational platforms. Scientific Visualization will be useful to practitioners of scientific visualization, students interested in both overview and advanced topics, and those interested in knowing more about the visualization process.
| Author | : Mourad Elloumi |
| Publisher | : Springer Nature |
| Total Pages | : 358 |
| Release | : 2021-07-13 |
| Genre | : Medical |
| ISBN | : 3030716767 |
This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
| Author | : Sujata Dash |
| Publisher | : John Wiley & Sons |
| Total Pages | : 450 |
| Release | : 2021-08-06 |
| Genre | : Computers |
| ISBN | : 1119711266 |
BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
| Author | : Matthew O. Ward |
| Publisher | : CRC Press |
| Total Pages | : 571 |
| Release | : 2015-06-11 |
| Genre | : Computers |
| ISBN | : 1482257386 |
An Updated Guide to the Visualization of Data for Designers, Users, and ResearchersInteractive Data Visualization: Foundations, Techniques, and Applications, Second Edition provides all the theory, details, and tools necessary to build visualizations and systems involving the visualization of data. In color throughout, it explains basic terminology
| Author | : Jude Hemanth |
| Publisher | : Springer |
| Total Pages | : 319 |
| Release | : 2019-08-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3030257975 |
This book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Data visualization and engineering has been instrumental in the development of many data-driven products and processes. As such the book promotes basic research on data visualization and knowledge engineering toward data engineering and knowledge. Visual data exploration focuses on perception of information and manipulation of data to enable even non-expert users to extract knowledge. A number of visualization techniques are used in a variety of systems that provide users with innovative ways to interact with data and reveal patterns. A variety of scalable data visualization techniques are required to deal with constantly increasing volume of data in different formats. Knowledge engineering deals with the simulation of the exchange of ideas and the development of smart information systems in which reasoning and knowledge play an important role. Presenting research in areas like data visualization and knowledge engineering, this book is a valuable resource for students, scholars and researchers in the field. Each chapter is self-contained and offers an in-depth analysis of real-world applications. It discusses topics including (but not limited to) spatial data visualization; biomedical visualization and applications; image/video summarization and visualization; perception and cognition in visualization; visualization taxonomies and models; abstract data visualization; information and graph visualization; knowledge engineering; human–machine cooperation; metamodeling; natural language processing; architectures of database, expert and knowledge-based systems; knowledge acquisition methods; applications, case studies and management issues: data administration issues and knowledge; tools for specifying and developing data and knowledge bases using tools based on communication aspects involved in implementing, designing and using KBSs in cyberspace; Semantic Web.
| Author | : Khalid Al-Jabery |
| Publisher | : Academic Press |
| Total Pages | : 312 |
| Release | : 2019-11-20 |
| Genre | : Technology & Engineering |
| ISBN | : 0128144831 |
Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor
