Statistical Bioinformatics (PDF)

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.