Overview
- Gives an introduction to computer-based methods for the analysis of genomic data
- Illustrated with examples
- Provides exercises and their solutions
Part of the book series: Statistics for Biology and Health (SBH)
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About this book
This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory.
Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions.
Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.
Keywords
Table of contents (13 chapters)
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Fitting Likelihood and Bayesian Models
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Exercises and Solutions
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Statistical Learning in Genetics
Book Subtitle: An Introduction Using R
Authors: Daniel Sorensen
Series Title: Statistics for Biology and Health
DOI: https://doi.org/10.1007/978-3-031-35851-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-35850-0Published: 20 September 2023
Softcover ISBN: 978-3-031-35853-1Due: 21 October 2023
eBook ISBN: 978-3-031-35851-7Published: 19 September 2023
Series ISSN: 1431-8776
Series E-ISSN: 2197-5671
Edition Number: 1
Number of Pages: XVI, 693
Number of Illustrations: 1 b/w illustrations
Topics: Statistical Theory and Methods, Statistics, general, Biostatistics, Genetics and Genomics