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.

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.

Biomedical Science in Professional and Clinical Practice is essential reading for all trainee biomedical scientists looking for an introduction to the biomedical science profession whether they are undergraduates following an accredited biomedical sciences BSc, graduate trainees or experienced staff with overseas qualifications. This book guides trainees through the subjects, which they need to understand to meet the standards required by the Health Professions Council for state registration. These include professional topics, laws and guidelines governing clinical pathology, basic laboratory techniques and an overview of each pathology discipline. It helps trainees at any stage of training and in any pathology discipline(s) to think creatively about how to gather evidence of their understanding and professional competence. By referring to specialist sources of information in each area, it helps students to explore particular topics in more depth and to keep up to date with professional and legal changes. It is also of value to any Training Officers who are looking for ideas while planning a programme of training for a trainee biomedical scientist. The book includes basic principles of working in the pathology laboratory including laws and regulations, which must be observed, such as health and safety, data protection and equal opportunities laws and guidelines. Practical exercises are included throughout the book with examples of coursework, suggestions for further exercises and self -assessment. Summary boxes of key facts are clearly set out in each chapter and ideas for group/tutorial discussions are also provided to enhance student understanding.

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

Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide. Whether you develop measurement instruments or use them in novel ways, this practical text will prepare you to be an effective generator and consumer of biomedical data. Designed for both classroom instruction and self-study, it explains how information is encoded into recorded data and can be extracted and displayed in an accessible manner. Describes and integrates experimental design, performance assessment, classification, and system modelling. Combines mathematical concepts with computational models, providing the tools needed to answer advanced biomedical questions. Includes MATLAB® scripts throughout to help readers model all types of biomedical systems, and contains numerous homework problems, with a solutions manual available online. This is an essential text for advanced undergraduate and graduate students in bioengineering, electrical and computer engineering, computer science, medical physics, and anyone preparing for a career in biomedical sciences and engineering.

Contemporary biomedical and clinical research is undergoing constant development thanks to the rapid advancement of various high throughput technologies at the DNA, RNA and protein levels. These technologies can generate vast amounts of raw data, making bioinformatics methodologies essential in their use for basic biomedical and clinical applications. Bioinformatics for biomedical science and clinical applications demonstrates what these cutting-edge technologies can do and examines how to design an appropriate study, including how to deal with data and address specific clinical questions. The first two chapters consider Bioinformatics and analysis of the human genome. The subsequent three chapters cover the introduction of Transcriptomics, Proteomics and Systems biomedical science. The remaining chapters move on to critical developments, clinical information and conclude with domain knowledge and adaptivity.

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.