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Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook

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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Predicting breast cancer outcomes using Random Forests

We are now going to predict the outcomes for some patients using Random Forests. A random forest is an ensemble method (it will use several instances of other machine learning algorithms) that uses many decision trees to arrive at robust conclusions about the data. We are going to use the same example as in the previous recipe: breast cancer traits and outcomes.

This recipe has two main goals: to introduce you to random forests and issues regarding the training of machine learning algorithms.

Getting ready

The code for this recipe can be found in Chapter10/Random_Forest.py.

How to do it…

Take a look at the code:

  1. We start, as in the previous recipe, by getting rid of samples with missing information:
    import pandas as pd
    import numpy as np
    import pandas as pd
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.tree import export_graphviz...

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