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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Length 396 pages
Edition 2nd Edition
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Author (1):
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Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse FREE CHAPTER 3. Chapter 3: ggplot2 and Extensions for Publication Quality Plots 4. Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites 5. Chapter 5: Easily Performing Statistical Tests Using Linear Models 6. Chapter 6: Performing Quantitative RNA-seq 7. Chapter 7: Finding Genetic Variants with HTS Data 8. Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs 9. Chapter 9: Phylogenetic Analysis and Visualization 10. Chapter 10: Analyzing Gene Annotations 11. Chapter 11: Machine Learning with mlr3 12. Chapter 12: Functional Programming with purrr and base R 13. Chapter 13: Turbo-Charging Development in R with ChatGPT 14. Index 15. Other Books You May Enjoy

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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