Book Image

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
Book Image

Bioinformatics with Python Cookbook - Third Edition

By: Tiago Antao

Overview of this book

Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data, and this book will show you how to manage these tasks using Python. This updated third edition of the Bioinformatics with Python Cookbook begins with a quick overview of the various tools and libraries in the Python ecosystem that will help you convert, analyze, and visualize biological datasets. Next, you'll cover key techniques for next-generation sequencing, single-cell analysis, genomics, metagenomics, population genetics, phylogenetics, and proteomics with the help of real-world examples. You'll learn how to work with important pipeline systems, such as Galaxy servers and Snakemake, and understand the various modules in Python for functional and asynchronous programming. This book will also help you explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks, including Dask and Spark. In addition to this, you’ll explore the application of machine learning algorithms in bioinformatics. By the end of this bioinformatics Python book, you'll be equipped with the knowledge you need to implement the latest programming techniques and frameworks, empowering you to deal with bioinformatics data on every scale.
Table of Contents (15 chapters)

Exploring breast cancer traits using Decision Trees

One of the first problems that we have when we receive a dataset is deciding what to start analyzing. At the very beginning, there is quite often a feeling of loss about what to do first. Here, we will present an exploratory approach based on Decision Trees. The big advantage of Decision Trees is that they will give us the rules that constructed the decision tree, allowing us a first tentative understanding of what is going on with our data.

In this example, we will be using a dataset with trait observations from patients with breast cancer. The dataset with 699 data entries includes information such as clump thickness, uniformity of cell size, or type of chromatin. The outcome is either a benign or malignant tumor. The features are encoded with values from 0 to 10. More information about the project can be found at

Getting ready

We are going...