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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook - Third Edition

By : Tiago Antao
4 (8)
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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

4 (8)
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)
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Using clustering over PCA to classify samples

PCA in genomics allows us to see how samples cluster. In many cases, individuals from the same population will be in the same area of the chart. But we would like to go further and predict where new individuals fall in terms of populations. To do that, we will start with PCA data, as it does dimensionality reduction – making working with the data easier – and then apply a K-Means clustering algorithm to predict where new samples fall. We will use the same dataset as in the recipe above. We will use all our samples save one to train the algorithm, and then we will predict where the remaining sample falls.

K-Means clustering can be an example of a supervised algorithm. In these types of algorithms, we need a training dataset so that the algorithm is able to learn. After training the algorithm, it will be able to predict a certain outcome for new samples. In our case, we are hoping that we can predict the population.

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