Book description
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.
Jae K. Lee, Ph. D., is a professor of
biostatistics and epidemiology in the Department of Health Evaluation
Sciences at the University of Virginia School of Medicine, where he
designed and teaches a course on Statistical Bioinformatics in
Medicine. He earned his doctorate in statistical genetics from the
University of Wisconsin, Madison. He was previously a research
scientist in the Laboratory of Molecular Pharmacology, National Cancer
Institute. Among his current research interests is the integration of
statistical and genomic information for the analysis of microarray
data.
