Road to statistical bioinformatics -- Probability concepts and distributions for analyzing large biological data -- Quality control of high-throughput biological data -- Statistical testing and significance for large biological data analysis -- Clustering : unsupervised learning in large biological data -- Classification : supervised learning with high-dimensional biological data -- Multidimensional analysis and visualization on large biological data -- Statistical models, inference, and algorithms for large biological data analysis -- Experimental designs on high-throughput biological experiments -- Statistical resampling techniques for large biological data analysis -- Statistical network analysis for biological systems and pathways -- Trends and statistical challenges in genomewide association studies -- R and bioconductor packages in bioinformatics : towards systems biology.

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. 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.