Applied Mathematics for the Analysis of Biomedical Data
Models, Methods, and MATLAB
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Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB(r) toolbox for the collection, visualization, and evaluation of experimental and real-life dataApplied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB(r) presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, indu...
Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB(r) toolbox for the collection, visualization, and evaluation of experimental and real-life data
Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB(r) presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government-sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real-world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods todetermine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing.
In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book's technical merit and value to industry professionals.
Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code.
In addition, the book features:
_ Real-world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen
_ Clear delineation of topics to accelerate access to data analysis
_ Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises
Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB(r) is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.
Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB(r) presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government-sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real-world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods todetermine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing.
In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book's technical merit and value to industry professionals.
Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code.
In addition, the book features:
_ Real-world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen
_ Clear delineation of topics to accelerate access to data analysis
_ Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises
Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB(r) is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.
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