Bioinformatics with R Cookbook

More Information
Learn
  • Retrieve biological data from within an R environment without hassling web pages
  • Annotate and enrich your data and convert the identifiers
  • Find relevant text from PubMed on which to perform text mining
  • Find phylogenetic relations between species
  • Infer relations between genomic content and diseases via GWAS
  • Classify patients based on biological or clinical features
  • Represent biological data with attractive visualizations, useful for publications and presentations
About

Bioinformatics is an interdisciplinary field that develops and improves upon the methods for storing, retrieving, organizing, and analyzing biological data. R is the primary language used for handling most of the data analysis work done in the domain of bioinformatics.

Bioinformatics with R Cookbook is a hands-on guide that provides you with a number of recipes offering you solutions to all the computational tasks related to bioinformatics in terms of packages and tested codes.

With the help of this book, you will learn how to analyze biological data using R, allowing you to infer new knowledge from your data coming from different types of experiments stretching from microarray to NGS and mass spectrometry.

Features
  • Use the existing R-packages to handle biological data
  • Represent biological data with attractive visualizations
  • An easy-to-follow guide to handle real-life problems in Bioinformatics like Next Generation Sequencing and Microarray Analysis
Page Count 340
Course Length 10 hours 12 minutes
ISBN 9781783283132
Date Of Publication 22 Jun 2014

Authors

Paurush Praveen Sinha

Paurush Praveen Sinha has been working with R for the past seven years. An engineer by training, he got into the world of bioinformatics and R when he started working as a research assistant at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Germany. Later, during his doctorate, he developed and applied various machine learning approaches with the extensive use of R to analyze and infer from biological data. Besides R, he has experience in various other programming languages, which include Java, C, and MATLAB. During his experience with R, he contributed to several existing R packages and is working on the release of some new packages that focus on machine learning and bioinformatics. In late 2013, he joined the Microsoft Research-University of Trento COSBI in Italy as a researcher. He uses R as the backend engine for developing various utilities and machine learning methods to address problems in bioinformatics.