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R for Medicine and BiologyAuthor: Paul D. Lewis
Part of the JONES AND BARTLETT SERIES IN BIOMEDICAL INFORMATICS
R is quickly becoming the number one choice for users in the fields of biology, medicine, and bioinformatics as their main means of storing, processing, sharing, and analysing biomedical data. R for Medicine and Biology is a step-by-step guide through the use of the statistical environment R, as used in a biomedical domain. Ideal for healthcare professionals, scientists, informaticists, and statistical experts, this resource will provide even the novice programmer with the tools necessary to process and analyse their data using the R environment. Introductory chapters guide readers in how to obtain, install, and become familiar with R and provide a clear introduction to the programming language using numerous worked examples. Later chapters outline how R can be used, not just for biomedical data analysis, but also as an environment for the processing, storing, reporting, and sharing of data and results. The remainder of the book explores areas of R application to common domains of biomedical informatics, including imaging, statistical analysis, data mining/modeling, pathology informatics, epidemiology, clinical trials, and metadata usage. R for Medicine and Biology will provide you with a single desk reference for the R environment and its many capabilities.
TABLE OF CONTENTS
Chapter 1 R installation and getting help
Chapter 2 The R environment and packages
Chapter 3 Basic Fundamentals of R
Chapter 4 Plotting data
Chapter 5 Example datasets
Chapter 6 Importing and exporting data in R
Chapter 7 R, SQL and database connectivity
Chapter 8 Using R to build a biomedical database in MySQL
Chapter 9 Creating heterogeneous datasets for analysis in R
Chapter 10 Descriptive statistics in R
Chapter 11 R and Basic Inferential Statistical Analysis
Chapter 12 Writing functions in R
Chapter 13 Multivariate analysis in R
Chapter 14 Survival analysis
Chapter 15 Data mining and predictive modeling with R and WEKA
Chapter 16 Surveillance of infectious disease
Chapter 17 Medical imaging and R
Chapter 18 Retrieving public microarray datasets
Chapter 19 Working with microarray data
Chapter 20 Annotating microarray gene lists
Chapter 21 Array CGH analysis
Chapter 22 XML for storing and sharing data