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)

Machine Learning for Bioinformatics

Machine learning is used in a wide variety of contexts and computational biology is not an exception. Machine learning has countless applications in the field, probably the oldest and most known being the use of Principal Component Analysis (PCA) to study population structure using genomics. There are many other potential applications as this is a burgeoning field. In this chapter, we are going to introduce machine learning concepts from a bioinformatics perspective.

Given that machine learning is a very complex topic that could easily fill a book, here we intend to take an intuitive approach that will allow you to broadly understand how some machine learning techniques can be useful to tackle biological problems. If you find these techniques useful, you will understand the fundamental concepts and can proceed to more detailed literature.

If you are using Docker, and because all the libraries in this chapter are fundamental for data analysis...