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Bioinformatics with R Cookbook [Englisch] [Taschenbuch]

Paurush Praveen Sinha

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23. Juni 2014

Over 90 practical recipes for computational biologists to model and handle real-life data using R


  • 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

In Detail

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.

What you will learn from this book

  • 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


This book is an easy-to-follow, stepwise guide to handle real life Bioinformatics problems. Each recipe comes with a detailed explanation to the solution steps. A systematic approach, coupled with lots of illustrations, tips, and tricks will help you as a reader grasp even the trickiest of concepts without difficulty.

Who this book is written for

This book is ideal for computational biologists and bioinformaticians with basic knowledge of R programming, bioinformatics and statistics. If you want to understand various critical concepts needed to develop your computational models in Bioinformatics, then this book is for you. Basic knowledge of R is expected.

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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.

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Die hilfreichsten Kundenrezensionen auf (beta) 3.0 von 5 Sternen  1 Rezension
0 von 3 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen The structure of the discussion is very helpful and easy to follow 9. August 2014
Von Arnold Salvacion - Veröffentlicht auf
The book BioInformatics with R Cookbook is a 340 pages book published by PACKT publishing last June. The book is intended for individuals working on the areas of biology and genetics. Most of the techniques and type of analysis (i.e. sequence, protein structure, microarray, etc.) discussed in the book are tailored for practitioners handling genomics data. A typical cookbook style material, the focus of the book is on how to implement the above mentioned techniques using R. The book also tackles procedure on how to connect with genomics databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology as through the Bioconductor platform. It also tackles access some cloud base implementation of R.
The structure of the discussion is very helpful and easy to follow. The How to Do it, How it works, and There’s more.. sequence of discussion provides readers a good guide and grasp on the techniques that are being discussed.
On the other hand, the book is somehow lacking in the discussion of the basics of R software (i.e. intro to the language and data type). The book assumes that the readers have already acquired these basic know how about the language.
Overall, the book is a good reference material especially for individuals dealing with data on biology and genomics.
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