Statistical Learning in Genetics: An Introduction Using R

This book provides an introduction to computer-based methods for analyzing genomic data, suitable for readers with a biological background who lack formal mathematical training. It covers inference based on likelihood and Bayesian methods, prediction for continuous and binary data, and non-parametric methods, with examples using the open-source package R.

Format: Hardback
Length: 693 pages
Publication date: 20 September 2023
Publisher: Springer International Publishing AG

This book offers an insightful introduction to computer-based approaches for analyzing genomic data, leveraging breakthroughs in molecular and computational biology. The emergence of vast data sets, encompassing millions of genetic markers for individuals coupled with medical records, has created an unparalleled resource for understanding the relationship between human genetic variation and human biology and disease. Similar advancements have been observed in animal and plant breeding, where genetic marker information is integrated with production traits. As statistical geneticists, it is crucial to adapt, construct, and implement models that can extract valuable information from these large-scale data. An initial step involves gaining a deep understanding of the methodology underlying probability models and acquiring the modern computer-intensive methods required for fitting these models.

The primary objective of this book is to guide readers, particularly those with a numerical biology background, in taking the first step toward developing analytic skills for conducting genomic research. It is tailored to address the needs of biologists who may not possess the formal mathematical background typically found in professional statisticians. Consequently, explanations and derivations are provided in greater detail, ensuring a comprehensive understanding of the topics. The book is written in a concise and engaging style, incorporating numerous examples to illustrate key concepts. A significant portion of the examples utilize the open-source package R, emphasizing its practicality and versatility. The R code necessary to solve the exercises is readily available, allowing students to implement the code on their own computers, further enhancing their theoretical understanding.

Part I of the book focuses on methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. It delves into the principles and assumptions underlying these approaches, as well as their practical applications in genomic data analysis. Part II explores prediction for continuous variables, discussing various regression models and their implementation using R.

In conclusion, this book serves as an invaluable resource for individuals seeking to delve into the realm of computer-based methods for analyzing genomic data. By providing a comprehensive introduction to probability models, computational techniques, and practical applications, it equips readers with the necessary skills to unlock the wealth of information hidden within these vast data sets. Whether you are a biologist, statistician, or researcher interested in genetics or genomics, this book will be a valuable companion on your journey of discovery.

Weight: 1222g
Dimension: 166 x 242 x 45 (mm)
ISBN-13: 9783031358500
Edition number: 1st ed. 2023