jay etchings a strategies in biomedical data science driving force for innovation

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Jay Etchings A. Strategies in Biomedical Data Science. Driving Force for Innovation

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.

4289.88 рублей

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Leland L. Smith Critical Issues in Biomedical Science. A Guide for Biochemistry and Molecular & Cell Biology Graduate

Книга "Critical Issues in Biomedical Science. A Guide for Biochemistry and Molecular & Cell Biology Graduate".

1039 рублей

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Tashi Namgyel, Sonam Rinzin, Sonam Yudon Promoting Science Learning amongst Grade VIII Students through Multiple Intelligence Teaching Strategies

Research Paper (postgraduate) from the year 2017 in the subject Pedagogy - School Pedagogics, Royal University of Bhutan, language: English, abstract: Quality science education amongst the youth has become crucial to survive in the ever challenging 21st century world. Stakeholders at different levels are concerned that science education in Bhutan is not up to expected level. Science teachers of School X also experienced students' poor performance. Predominance of teacher centered mode of instruction (traditional method) is one factor that hindered science learning.Various literatures have cited the inherent potential of Multiple Intelligence (MI) teaching strategies to promote science learning, and provide students the learning platform depending upon their well-developed intelligence.This study was intended to promote science learning amongst the grade VIII students of School X through MI teaching strategies. 37 students were randomly sampled for the study and 11 students were selected from the sample population for the in-depth interview. The data were collected twice (baseline data before intervention with MI strategies, and post-line after the MI intervention strategies).Science teachers intervened with teaching strategies based on eight intelligences for the period of two months addressing the learning barriers identified from base line data. The base line and post line data were analyzed and compared. The study revealed that MI teaching strategies upscale the scienc...

2164 рублей

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Murad Ferid Collaborative Innovation in Drug Discovery. Strategies for Public and Private Partnerships

Can academia save the pharmaceutical industry? The pharmaceutical industry is at a crossroads. The urgent need for novel therapies cannot stem the skyrocketing costs and plummeting productivity plaguing R&D, and many key products are facing patent expiration. Dr. Rathnam Chaguturu presents a case for collaboration between the pharmaceutical industry and academia that could reverse the industry's decline. Collaborative Innovation in Drug Discovery: Strategies for Public and Private Partnerships provides insight into the potential synergy of basing R&D in academia while leaving drug companies to turn hits into marketable products. As Founder and CEO of iDDPartners, focused on pharmaceutical innovation, Founding president of the International Chemical Biology Society, and Senior Director-Discovery Sciences, SRI International, Dr. Chaguturu has assembled a panel of experts from around the world to weigh in on issues that affect the two driving forces in medical advancement. Gain global perspectives on the benefits and potential issues surrounding collaborative innovation Discover how industries can come together to prevent another «Pharma Cliff» Learn how nonprofits are becoming the driving force behind innovation Read case studies of specific academia-pharma partnerships for real-life examples of successful collaboration Explore government initiatives that help foster cooperation between industry and academia Dr. Chaguturu’s thirty-five years of experience in academia and industry, managing new lead discovery projects and forging collaborative partnerships with academia, disease foundations, nonprofits, and government agencies lend him an informative perspective into the issues facing pharmaceutical progress. In Collaborative Innovation in Drug Discovery: Strategies for Public and Private Partnerships, he and his expert team provide insight into the various nuances of the debate.

11087.68 рублей

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Zacharias Voulgaris Data Science. Mindset, Methodologies, and Misconceptions

Master the concepts and strategies underlying success and progress in data science.From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientist’s toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework.The following chapters cover these four foundational areas: Chapter 1 - What Is Data Science? Chapter 2 - The Data Science Pipeline Chapter 3 - Data Science Methodologies Chapter 4 - The Data Scientist’s Toolbox Chapter 5 - Questions to Ask and the Hypotheses They Are Based On Chapter 6 - Data Science Experiments and Evaluation of Their Results Chapter 7 - Sensitivity Analysis of Experiment Conclusions Chapter 8 - Programming Bugs Chapter 9 - Mistakes Through the Data Science Process Chapter 10 - Dealing with Bugs and Mistakes Effectively and Efficiently Chapter 11 - The Role of Heuristics in Data Science Chapter 12 - The Role of AI in Data Science Chapter 13 - Data Science Ethics Chapter 14 - Future Trends and How to Remain RelevantTargeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and ob...

3977 рублей

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Rodney R. Dietert, Janice Dietert Science Sifting. Tools for Innovation in Science and Technology

Книга "Science Sifting. Tools for Innovation in Science and Technology".

4502 рублей

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Jason Saul Social Innovation, Inc. 5 Strategies for Driving Business Growth through Social Change

Could Wal-Mart offer a better solution to healthcare than Medicaid? Could GE help reduce global warming faster than the Kyoto protocol? Social Innovation, Inc. declares a new era where companies profit from social change. Leading corporations like GE, Wellpoint, Travelers and Wal-Mart are transforming social responsibility into social innovation and revolutionizing the way we think about the role of business in society. Based on four years of measuring the social strategies of America's leading corporations, Jason Saul lays out the five strategies for social innovation and offers a practical roadmap for how to get started. Explains the fundamental shift in the role of business in society, from social contract to social capital market Identifies the 5 social innovation strategies: submarket products and services, social points of entry, pipeline talent, reverse lobbying, and emotive customer bonding Offers step-by-step guidance for creating economic value through positive social change Social Innovation, Inc. is about making social change work for the business, and in turn staying relevant in the new economy.

1846.18 рублей

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Evan Stubbs Big Data, Big Innovation. Enabling Competitive Differentiation through Business Analytics

A practical guide to leveraging your data to spur innovation and growth Your business generates reams of data, but what do you do with it? Reporting is only the beginning. Your data holds the key to innovation and growth – you just need the proper analytics. In Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics, author Evan Stubbs explores the potential gold hiding in your un-mined data. As Chief Analytics Officer for SAS Australia/New Zealand, Stubbs brings an industry insider's perspective to guide you through pattern recognition, analysis, and implementation. Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics details a groundbreaking approach to ensuring your company's upward trajectory. Use this guide to leverage your customer information, financial reports, performance metrics, and more to build a rock-solid foundation for future growth. Build an effective analytics team, and empower them with the right tools Learn how big data drives both evolutionary and revolutionary innovation, and who should be responsible Identify data collection and analysis opportunities and implement action plans Design the platform that suits your company's current and future needs Quantify performance with statistics, programming, and research for a more complete picture of operations Effective management means combining data, people, and analytics to create a synergistic force for innovation and growth. If you want your company to move forward with confidence, Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics can show you how to use what you already have and acquire what you need to succeed.

3959.89 рублей

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Malcolm Atkinson The Data Bonanza. Improving Knowledge Discovery in Science, Engineering, and Business

Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

8758.26 рублей

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Darius Dziuda M. Data Mining for Genomics and Proteomics. Analysis of Gene and Protein Expression Data

Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

8707.79 рублей

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John Bessant Responsible Innovation. Managing the Responsible Emergence of Science and Innovation in Society

Science and innovation have the power to transform our lives and the world we live in – for better or worse – in ways that often transcend borders and generations: from the innovation of complex financial products that played such an important role in the recent financial crisis to current proposals to intentionally engineer our Earth’s climate. The promise of science and innovation brings with it ethical dilemmas and impacts which are often uncertain and unpredictable: it is often only once these have emerged that we feel able to control them. How do we undertake science and innovation responsibly under such conditions, towards not only socially acceptable, but socially desirable goals and in a way that is democratic, equitable and sustainable? Responsible innovation challenges us all to think about our responsibilities for the future, as scientists, innovators and citizens, and to act upon these. This book begins with a description of the current landscape of innovation and in subsequent chapters offers perspectives on the emerging concept of responsible innovation and its historical foundations, including key elements of a responsible innovation approach and examples of practical implementation. Written in a constructive and accessible way, Responsible Innovation includes chapters on: Innovation and its management in the 21st century A vision and framework for responsible innovation Concepts of future-oriented responsibility as an underpinning philosophy Values – sensitive design Key themes of anticipation, reflection, deliberation and responsiveness Multi – level governance and regulation Perspectives on responsible innovation in finance, ICT, geoengineering and nanotechnology Essentially multidisciplinary in nature, this landmark text combines research from the fields of science and technology studies, philosophy, innovation governance, business studies and beyond to address the question, “How do we ensure the responsible emergence of science and innovation in society?”

11907.76 рублей

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Nii Attoh-Okine O. Big Data and Differential Privacy. Analysis Strategies for Railway Track Engineering

A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for railway track engineering, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering addresses common issues with the implementation of big data applications while exploring the limitations, advantages, and disadvantages of more conventional methods. In addition, the book provides a unifying approach to analyzing large volumes of data in railway track engineering using an array of proven methods and software technologies. Dr. Attoh-Okine considers some of today’s most notable applications and implementations and highlights when a particular method or algorithm is most appropriate. Throughout, the book presents numerous real-world examples to illustrate the latest railway engineering big data applications of predictive analytics, such as the Union Pacific Railroad’s use of big data to reduce train derailments, increase the velocity of shipments, and reduce emissions. In addition to providing an overview of the latest software tools used to analyze the large amount of data obtained by railways, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering: • Features a unified framework for handling large volumes of data in railway track engineering using predictive analytics, machine learning, and data mining • Explores issues of big data and differential privacy and discusses the various advantages and disadvantages of more conventional data analysis techniques • Implements big data applications while addressing common issues in railway track maintenance • Explores the advantages and pitfalls of data analysis software such as R and Spark, as well as the Apache™ Hadoop® data collection database and its popular implementation MapReduce Big Data and Differential Privacy is a valuable resource for researchers and professionals in transportation science, railway track engineering, design engineering, operations research, and railway planning and management. The book is also appropriate for graduate courses on data analysis and data mining, transportation science, operations research, and infrastructure management. NII ATTOH-OKINE, PhD, PE is Professor in the Department of Civil and Environmental Engineering at the University of Delaware. The author of over 70 journal articles, his main areas of research include big data and data science; computational intelligence; graphical models and belief functions; civil infrastructure systems; image and signal processing; resilience engineering; and railway track analysis. Dr. Attoh-Okine has edited five books in the areas of computational intelligence, infrastructure systems and has served as an Associate Editor of various ASCE and IEEE journals.

10372.01 рублей

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Lillian Pierson Data Science For Dummies

Discover how data science can help you gain in-depth insight into your business – the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

1980.92 рублей

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Mustafa Guler O. Therapeutic Nanomaterials

Addressing a cutting-edge, multidisciplinary field, this book reviews nanomaterials and their biomedical applications. It covers regeneration, implants, adhesives, and biosensors and strategies for more efficient therapy, diagnosis, and drug delivery with the use of nanotechnology. • Addresses the increasing demand for nanomedicine in a cutting-edge, multidisciplinary field • Introduces concepts, strategies, and requirements of developing materials • Discusses hot topics in drug delivery, such as neural regeneration, cartilage regeneration, bone tissue regeneration, dental regeneration, biomedical imaging, tissue adhesives and biosensors • Includes a chapter about nanotoxicology to help readers further understand the biocompatability of nanomaterials

11523.64 рублей

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Jae Lee K. Statistical Bioinformatics. For Biomedical and Life Science Researchers

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.

10133.35 рублей

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Ann Wheeler Standard and Super-Resolution Bioimaging Data Analysis. A Primer

A comprehensive guide to the art and science of bioimaging data acquisition, processing and analysis Standard and Super-Resolution Bioimaging Data Analysis gets newcomers to bioimage data analysis quickly up to speed on the mathematics, statistics, computing hardware and acquisition technologies required to correctly process and document data. The past quarter century has seen remarkable progress in the field of light microscopy for biomedical science, with new imaging technologies coming on the market at an almost annual basis. Most of the data generated by these systems is image-based, and there is a significant increase in the content and throughput of these imaging systems. This, in turn, has resulted in a shift in the literature on biomedical research from descriptive to highly-quantitative. Standard and Super-Resolution Bioimaging Data Analysis satisfies the demand among students and research scientists for introductory guides to the tools for parsing and processing image data. Extremely well illustrated and including numerous examples, it clearly and accessibly explains what image data is and how to process and document it, as well as the current resources and standards in the field. A comprehensive guide to the tools for parsing and processing image data and the resources and industry standards for the biological and biomedical sciences Takes a practical approach to image analysis to assist scientists in ensuring scientific data are robust and reliable Covers fundamental principles in such a way as to give beginners a sound scientific base upon which to build Ideally suited for advanced students having only limited knowledge of the mathematics, statistics and computing required for image data analysis An entry-level text written for students and practitioners in the bioscience community, Standard and Super-Resolution Bioimaging Data Analysis de-mythologises the vast array of image analysis modalities which have come online over the past decade while schooling beginners in bioimaging principles, mathematics, technologies and standards.

6914.18 рублей

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Connected Science. Strategies for Integrative Learning in College

Книга "Connected Science. Strategies for Integrative Learning in College".

3689 рублей

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Peter Costa J. Applied Mathematics for the Analysis of Biomedical Data. Models, Methods, and MATLAB

Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® 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® 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 to determine 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® 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.

9603.03 рублей

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Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® 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® 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 to determine 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® 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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