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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789950694
Length 316 pages
Edition 1st Edition
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Authors (2):
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Dr Dan Maclean Dr Dan Maclean
Author Profile Icon Dr Dan Maclean
Dr Dan Maclean
Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (13) Chapters Close

Preface 1. Performing Quantitative RNAseq 2. Finding Genetic Variants with HTS Data FREE CHAPTER 3. Searching Genes and Proteins for Domains and Motifs 4. Phylogenetic Analysis and Visualization 5. Metagenomics 6. Proteomics from Spectrum to Annotation 7. Producing Publication and Web-Ready Visualizations 8. Working with Databases and Remote Data Sources 9. Useful Statistical and Machine Learning Methods 10. Programming with Tidyverse and Bioconductor 11. Building Objects and Packages for Code Reuse 12. Other Books You May Enjoy

Machine learning for novel feature detection in proteins

Sometimes, we'll have a list of protein sequences that have come from some analysis or experiment that are in some way biologically related—for example, they may all bind the same target—and we will want to determine the parts of those proteins that are responsible for the action. Domain and motif finding, as we've done in the preceding recipes, can be helpful, but only if we've seen the domains before or the sequence is particularly well conserved or statistically over-represented. A different approach is to try machine learning in which we build a model that can classify our proteins of interest accurately and then use the properties of the model to show us which parts of the proteins result in the classification. We'll take that approach in this recipe; specifically, we'll train...

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