New book: Statistical Learning in Genetics - An Introduction Using R
One of the grand old men of statistical genetics, professor emeritus Daniel Sorensen from Center for Quantitative Genetics and Genomics (QGG), recently put the finishing touches to his latest book, in which he is sharing his experience from a long career in research.
The book provides an introduction to computer-based methods for the analysis of genomic data, and is divided in three parts.
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.
The target group is numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, the book contains considerably more detail in explanations and derivations.
Daniel Sorensen Statistical Learning in Genetics - An Introduction Using R. Springer Publishing. Release date 6 September 2023. It is already possible to pre-order the book in your preferred bookstore.