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)

Defining a task and learner to implement k-nearest neighbors (k-NNs) in mlr3

The k-nearest neighbors (k-NN) classification is a non-parametric ML algorithm used for classifying data points based on their proximity to other labeled data points. The algorithm determines the class membership of an unlabeled data point by examining the classes of its k-NNs in the feature space. The dataset consists of labeled data points, where each data point has a set of features (attributes) and belongs to a specific class or category. The value of k represents the number of nearest neighbors to consider for classification. It is typically chosen based on cross-validation or other model selection techniques. The algorithm measures the distance between the unlabeled data point and all the labeled data points in the feature space. The most commonly used distance metric is Euclidean distance. The k data points with the shortest distances to the unlabeled point are identified as its nearest neighbors. The...