Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
Table of Contents (16 chapters)

Machine Learning with mlr3

Machine learning (ML) is a broad term that covers a wide range of bioinformatic and data science activities including, regression, classification, and data clustering.

The mlr3 package is an open source ML framework for the R programming language. It is designed to provide a unified and efficient interface for building, evaluating, and comparing ML models. mlr3 is built on top of the mlr package, which is one of the most popular ML packages in R.

mlr3 follows a modular design, which means that different components of the ML process, such as data preprocessing, feature selection, model training, and model evaluation, are separated into individual objects. This design allows for greater flexibility and modularity, enabling users to easily customize and extend the functionality of the framework. We will look at this framework through consecutive classification and test steps in this chapter.

In this chapter, we will cover the following recipes:

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