Название: Statistical Learning in Genetics: An Introduction Using RАвтор: Daniel Sorensen
Издательство: Springer
Год: 2023
Страниц: 696
Язык: английский
Формат: pdf (true)
Размер: 16.7 MB
This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. 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. Most of today’s students are competent in R and there are many tutorials online for the uninitiated. The R-code needed to solve the exercises is provided in all cases and is written, with few exceptions, with the objective of being transparent rather than efficient.
