Full Description
Statistical Bioinformatics with R, Second Edition offers a balanced treatment of statistical theory within the context of bioinformatics applications. Designed for a one- or two-semester senior undergraduate or graduate statistical bioinformatics course, this text provides a comprehensive overview of the statistical methods that can be used to analyze bioinformatics data including omics and single cell-RNA seq data. It 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. Statistical Bioinformatics with R, Second Edition expands upon the original material by incorporating the latest advancements in bioinformatics and statistical methodologies, including new chapters and sections that explore 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. Ancillary materials including PowerPoint lectures for both students and instructors and an Instructors Manual provide support for students across upper-level undergraduate and graduate courses in bioinformatics, computational biology, and biostatistics.
Contents
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
