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R Bioinformatics Cookbook. Utilize R packages for bioinformatics, genomics, data science, and machine learning - Second Edition

Język publikacji: 1
Autor:
Dan MacLean
R Bioinformatics Cookbook. Utilize R packages for bioinformatics, genomics, data science, and machine learning - Second Edition Dan MacLean - okladka książki

R Bioinformatics Cookbook. Utilize R packages for bioinformatics, genomics, data science, and machine learning - Second Edition Dan MacLean - okladka książki

Serie wydawnicze:
Cookbook
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
396
Dostępne formaty:
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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.
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O autorze książki

Professor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now a Honorary Professor in the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics, specializing in R and Bioconductor and developing analytical workflows in bioinformatics, genomics, genetics, image analysis, and proteomics at The Sainsbury Laboratory since 2006. Dan has developed and published software packages in R, Ruby, and Python with over 100,000 downloads combined.

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