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.
* Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics
* Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences
* Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis
* Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis
* Offers programming examples and datasets
* Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material
* Features supplementary materials, including datasets, links, and a statistical package available online
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.
Produkt-Info
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Maße
Höhe: 231 mm
Breite: 155 mm
Dicke: 23 mm
Gewicht
ISBN-13
978-0-471-69272-0 (9780471692720)
Schweitzer Klassifikation
Jae K. Lee, Ph.D., is a professor of biostatistics and epidemiology in the Department of Health Evaluation Sciences at the University of Virginia School of Medicine, where he designed and teaches a course on Statistical Bioinformatics in Medicine. He earned his doctorate in statistical genetics from the University of Wisconsin, Madison. He was previously a research scientist in the Laboratory of Molecular Pharmacology, National Cancer Institute. Among his current research interests is the integration of statistical and genomic information for the analysis of microarray data.
Chapter 1: Road to Statistical Bioinformatics
Chapter 2: Probability concepts and distributions for analyzing large biological data
Chapter 3: Quality control of high throughput biological data
Chapter 4: Statistical testing and significance for large biological data analysis
Chapter 5: Advance statistical modeling and inference on large biological data
Chapter 6: Clustering: unsupervised learning in large screening biological data
Chapter 7: Classification: supervised learning in large screening biological data
Chapter 8: Multi-dimensional analysis and visualization on large biological data
Chapter 9: Experimental designs on high throughput biological experiments
Chapter 10: Statistical resampling techniques for large biological data analysis
Chapter 11: Statistical network analysis for biological systems and pathways
Chapter 12: Advances in current statistical genetics and association studies
Chatper 13: R and Bioconductor packages in bioinformatics
