Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB

Features a practical approach to the analysis of biomedical data via mathematical models and provides a MATLAB® toolbox for the collection, visualization, and analysis of experimental and real–life data that occur in the biological sciences

Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task biological scientists face when asked to provide mathematical models to fit collected data.  The primary focus is on the application of data, mathematics, and scientific computing to development models that provide insight into biological systems. The author draws upon his experience in academics, industry, and government sponsored research along with his familiarity with 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, and 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 an understanding of MATLAB programming.  Moreover, the associated toolbox permits readers to understand the process of data analysis without needing to delve deeply into the mathematical theory.  This permits 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 A Solutions Manual containing solutions to select exercises, and a related website contains the MATLAB toolbox created for this book

Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, quantitative and computational biology, mathematical biology, mathematical modeling, and the life and social sciences. 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.

Peter J. Costa, PhD, is Senior Applied Mathematician at Hologic Incorporated in Marlborough, MA. Dr. Costa is the co–creator of MATLAB′s Symbolic Math Toolbox.  He has developed mathematical methods for the spread of HIV, the outbreak of AIDS, the transmission of an infectious respiratory disease throughout a population, and the diagnosis of cervical cancer.  His research interests include scientific computing and mathematical biology, and he received his PhD in Applied Mathematics from the University of Massachusetts at Amherst.

Preface

Acknowledgements

In Memoriam

In Dedication

Introduction

Chapter 1: Data

1.1. Data Visualization

1.2. Data Transformations

1.3. Data Filtering

1.4. Data Clustering

1.5. Data Quality and Data Cleaning

References

Chapter 2: Some Examples

2.1. Glucose Insulin Interaction

2.2. Transition from HIV to AIDS

2.3. Real Time Polymerase Chain Reaction

References

Chapter 3: SEIR Models

3.1. Transmission of HIV/AIDS

3.2. Modelling the Transmission of a Respiratory Epidemic

3.3. Predicting the Outbreak of a Respiratory Epidemic

References

Additional References (HIV/AIDS)

Chapter 4: Statistical Pattern Recognition and Classification

4.1. Measurements and Data Classes

4.2. Data Preparation, Normalization, and Weighting Matrix

4.3. Principal Components

4.4. Discriminant Analysis

4.5. Regularized Discriminant Analysis and Classification

4.6. The Confusion Matrix, Receiver Operator Characteristic Curves, and Assessment Metrics

4.7. An Example

4.8. Nonlinear Methods

References

Chapter 5: Biostatistics and Hypothesis Testing

5.1. Hypothesis Testing Framework

5.2. Test of Means

5.3. Tests of Proportions

5.4. Tests of Variances

5.5. Other Hypothesis Tests

References

Chapter 6: Clustered Data and Analysis of Variance

6.1. Clustered Matched pair Data and Non inferiority

6.2. Clustered Data, Assessment Metrics, and Diagnostic Likelihood Ratios

6.3. Relative Diagnostic Likelihood Ratios

6.4. Analysis of Variance for Clustered Data

6.5. Examples for ANOVA

6.6. Bootstrapping and Confidence Intervals

References

Appendix: Mathematical Matters

Glossary of MATLAB Functions

Index