An Introduction to Statistics and Data Analysis for Bioinformatics using R
1st Edition
Published on 31. December 2023
Book
Hardback
506 pages
978-1-4398-9236-7 (ISBN)
Description
From the very basics to linear models, this book provides a complete introduction to statistics, data analysis, and R for bioinformatics research and applications. It covers ANOVA, cluster analysis, visualization tools, and machine learning techniques. Suitable for self-study and courses in computational biology, bioinformatics, statistics, and the life sciences, the text also presents examples of microarrays and bioinformatics applications. R code illustrates all of the essential concepts and is available on an accompanying CD-ROM.
More details
Series
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Academic and Professional Practice & Development
Illustrations
100 s/w Abbildungen
100 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-4398-9236-7 (9781439892367)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Person
Sorin Draghici the Robert J. Sokol MD Endowed Chair in Systems Biology in the Department of Obstetrics and Gynecology, professor in the Department of Clinical and Translational Science and Department of Computer Science, and head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He is also the chief of the Bioinformatics and Data Analysis Section in the Perinatology Research Branch of the National Institute for Child Health and Development. A senior member of IEEE, Dr. Draghici is an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. He earned a Ph.D. in computer science from the University of St. Andrews.
Content
Introduction. Introduction to R. Bioconductor: Principles and Illustrations. Elements of Statistics. Probability Distributions. Basic Statistics in R. Statistical Hypothesis Testing. Classical Approaches to Data Analysis. Analysis of Variance (ANOVA). Linear Models in R. Experiment Design. Multiple Comparisons. Analysis and Visualization Tools. Cluster Analysis. Machine Learning Techniques. The Road Ahead.