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.Content:
Chapter 1 Road to Statistical Bioinformatics (pages 1–6): Jae K. Lee
Chapter 2 Probability Concepts and Distributions for Analyzing Large Biological Data (pages 7–55): Sooyoung Cheon
Chapter 3 Quality Control of High?Throughput Biological Data (pages 57–70): Paul D. Williams
Chapter 4 Statistical Testing and Significance for Large Biological Data Analysis (pages 71–88): Hyung Jun Cho and Wonseok Seo
Chapter 5 Clustering: Unsupervised Learning in Large Biological Data (pages 89–127): Nabil Belacel, Christa Wang and Miroslava Cupelovic?Culf
Chapter 6 Classification: Supervised Learning with High?Dimensional Biological Data (pages 129–156): Hongshik Ahn and Hojin Moon
Chapter 7 Multidimensional Analysis and Visualization on Large Biomedical Data (pages 157–184): Jinwook Seo and Ben Shneiderman
Chapter 8 Statistical Models, Inference, and Algorithms for Large Biological Data Analysis (pages 185–199): Debashis Ghosh, Seungyeoun Lee and Taesung Park
Chapter 9 Experimental Designs on High?Throughput Biological Experiments (pages 201–217): Xiangqin Cui
Chapter 10 Statistical Resampling Techniques for Large Biological Data Analysis (pages 219–248): Annette M. Molinaro and Karen Lostritto
Chapter 11 Statistical Network Analysis for Biological Systems and Pathways (pages 249–282): Youngchul Kim, Jae K. Lee, Haseong Kim, Annamalai Muthiah and Ginger Davis
Chapter 12 Trends and Statistical Challenges in Genomewide Association Studies (pages 283–308): Ning Sun and Hongyu Zhao
Chapter 13 R and Bioconductor Packages in Bioinformatics: Towards Systems Biology (pages 309–338): Nolweim LeMeur, Michael Lawrence, Merav Bar, Muneesh Tewari and Robert Gentleman