Statistical Bioinformatics with R,

Edition 2

Editors: By Sunil K. Mathur

Publication Date: 01 Aug 2026

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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.

Key Features

Integrates biological, statistical, and computational concepts
Provides coverage of complex statistical methods in context 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

About the author

By Sunil K. Mathur, Director, Statistical Computing and Consulting CenterUniversity of Mississippi, Oxford, USA

1. Introduction
2. Fundamentals of Molecular Biology
3. Exploratory Data Analysis
4. Statistical Methods for Bioinformatics
5. Bayesian Methods in Bioinformatics
6. AI/Machine Learning in Bioinformatics
7. Sequence Analysis
8. Genomic Data Analysis
9. Transcriptomics Data Analysis
10. Transcriptomics Data Analysis
11. Metabolomics

Book Reviews

Review of the previous edition:
"Students and biologists who want to specialize in the fast-paced field of bioinformatics should read this book. Mathur brings together a comprehensive and very practical view of the field. He combines sufficient mathematical proofs with hints and suggestions, and provides many real examples taken directly from the genetics, proteomics, and molecular biology fields…Many other bioinformatics topics—for example, clustering algorithms, specialized R packages, or the challenges of analyzing mass-spectrometry data—are only alluded to and not covered fully in the book. However, in its entirety, this is a very useful, clearly written introduction to statistical bioinformatics with R. It contains many real examples, and would be a help to those starting out in the field."—Computing Reviews.com

ISBN: 9780443404375

Page Count: 350

Retail Price :

Students in upper-level undergraduate and graduate courses in bioinformatics, computational biology, and biostatistics