Introduction To Biomedical Data Science
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| Author | : Robert Hoyt |
| Publisher | : Lulu.com |
| Total Pages | : 260 |
| Release | : 2019-11-24 |
| 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 | : Rafael A. Irizarry |
| Publisher | : CRC Press |
| Total Pages | : 537 |
| Release | : 2016-10-04 |
| Genre | : Mathematics |
| ISBN | : 1498775861 |
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.
| 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
| Author | : Sarah J. Pitt |
| Publisher | : John Wiley & Sons |
| Total Pages | : 241 |
| Release | : 2013-04-03 |
| Genre | : Science |
| ISBN | : 111868771X |
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.
| Author | : Michael Insana |
| Publisher | : Cambridge University Press |
| Total Pages | : 405 |
| Release | : 2021-06-17 |
| Genre | : Technology & Engineering |
| ISBN | : 1316832767 |
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.
| Author | : John Enderle |
| Publisher | : Elsevier |
| Total Pages | : 1141 |
| Release | : 2005-05-20 |
| Genre | : Science |
| ISBN | : 0080473148 |
Under the direction of John Enderle, Susan Blanchard and Joe Bronzino, leaders in the field have contributed chapters on the most relevant subjects for biomedical engineering students. These chapters coincide with courses offered in all biomedical engineering programs so that it can be used at different levels for a variety of courses of this evolving field. Introduction to Biomedical Engineering, Second Edition provides a historical perspective of the major developments in the biomedical field. Also contained within are the fundamental principles underlying biomedical engineering design, analysis, and modeling procedures. The numerous examples, drill problems and exercises are used to reinforce concepts and develop problem-solving skills making this book an invaluable tool for all biomedical students and engineers. New to this edition: Computational Biology, Medical Imaging, Genomics and Bioinformatics.* 60% update from first edition to reflect the developing field of biomedical engineering* New chapters on Computational Biology, Medical Imaging, Genomics, and Bioinformatics* Companion site: http://intro-bme-book.bme.uconn.edu/* MATLAB and SIMULINK software used throughout to model and simulate dynamic systems* Numerous self-study homework problems and thorough cross-referencing for easy use
| Author | : Gareth James |
| Publisher | : Springer Nature |
| Total Pages | : 617 |
| Release | : 2023-08-01 |
| Genre | : Mathematics |
| ISBN | : 3031387473 |
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
| Author | : Kun Chang Lee |
| Publisher | : Academic Press |
| Total Pages | : 298 |
| Release | : 2020-10-18 |
| Genre | : Science |
| ISBN | : 0128193158 |
Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. - Examines the development and application of data analytics applications in biomedical data - Presents innovative classification and regression models for predicting various diseases - Discusses genome structure prediction using predictive modeling - Shows readers how to develop clinical decision support systems - Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks
| Author | : Joseph DuBovy |
| Publisher | : Gregg Division McGraw-Hill |
| Total Pages | : 408 |
| Release | : 1978 |
| Genre | : Medical |
| ISBN | : |
This book discusses a basic exploration of the biomedical frequency spectrum and its physiochemical origins, and how physiological data are changed into electric signals or amplified.
| Author | : Julie Vu |
| Publisher | : |
| Total Pages | : |
| Release | : 2020-03 |
| Genre | : |
| ISBN | : 9781943450114 |
Introduction to Statistics for the Life and Biomedical Sciences has been written to be used in conjunction with a set of self-paced learning labs. These labs guide students through learning how to apply statistical ideas and concepts discussed in the text with the R computing language.The text discusses the important ideas used to support an interpretation (such as the notion of a confidence interval), rather than the process of generating such material from data (such as computing a confidence interval for a particular subset of individuals in a study). This allows students whose main focus is understanding statistical concepts to not be distracted by the details of a particular software package. In our experience, however, we have found that many students enter a research setting after only a single course in statistics. These students benefit from a practical introduction to data analysis that incorporates the use of a statistical computing language.In a classroom setting, we have found it beneficial for students to start working through the labs after having been exposed to the corresponding material in the text, either from self-reading or through an instructor presenting the main ideas. The labs are organized by chapter, and each lab corresponds to a particular section or set of sections in the text.There are traditional exercises at the end of each chapter that do not require the use of computing. In the current posting, Chapters 1 - 5 have end-of-chapter exercises. More complicated methods, such as multiple regression, do not lend themselves to hand calculation and computing is necessary for gaining practical experience with these methods. The lab exercises for these later chapters become an increasingly important part of mastering the material.An essential component of the learning labs are the "Lab Notes" accompanying each chapter. The lab notes are a detailed reference guide to the R functions that appear in the labs, written to be accessible to a first-time user of a computing language. They provide more explanation than available in the R help documentation, with examples specific to what is demonstrated in the labs.
