Statistical Bioinformatics with R, Second Edition offers a balanced treatment of statistical theory within the context of bioinformatics applications. The book goes beyond gene expression and sequence analysis to include a careful integration of statistical theory in bioinformatics. The inclusion of R codes, along with the development of advanced methodologies such as Bayesian and Markov models, equips students with a solid foundation for conducting bioinformatics research. Sections incorporate the latest advancements in bioinformatics and statistical methodologies, including new chapters on cutting-edge topics such as high-throughput sequencing data analysis, AI/machine learning applications in bioinformatics, and advanced statistical methods.
From new and updated practical examples and case studies that illustrate real-world applications of statistical techniques to bioinformatic problems, to enhanced end-of-chapter exercises, detailed code annotations, and an improved companion website with supplementary materials, including datasets and R scripts, this book is a valuable resource for both self-study and formal coursework, fostering a deeper understanding of statistical bioinformatics and equipping readers with the skills needed to tackle complex biological data analysis challenges.
