Statistical Bioinformatics with R

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.

Features

Integrates biological, statistical, and computational concepts

Provides coverage of complex statistical methods with applications in bioinformatics for advanced technological data

Presents exercises and examples, including R codes, to aid teaching and learning

Covers Bayesian methods and modern testing principles in one convenient book

Includes PowerPoint lectures for student and instructor use, as well as an Instructors Manual