Deep Learning for Genomics

This is the code repository for Deep Learning for Genomics, published by Packt.

Data-driven approaches for genomics applications in life sciences and biotechnology

What is this book about?

Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you’ll learn about

conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets.

By the end of this book, you’ll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.

This book covers the following exciting features:

• Discover the machine learning applications for genomics
• Explore deep learning concepts and methodologies for genomics applications
• Understand supervised deep learning algorithms for genomics applications
• Get to grips with unsupervised deep learning with autoencoders
• Improve deep learning models using generative models
• Operationalize deep learning models from genomics datasets
• Visualize and interpret deep learning models
• Understand deep learning challenges, pitfalls, and best practices

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders.

The code will look like the following:

# covid19_features.py
from Bio import SeqIO

Following is what you need for this book: This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.

With the following software and hardware list you can run all code files present in the book (Chapter 1-12).

Software and Hardware List

Related products

• Machine Learning with Amazon SageMaker Cookbook [Packt] [Amazon]

• Amazon Redshift Cookbook [Packt] [Amazon]

Get to Know the Author

Dr. Upendra Kumar Devisetty has a Ph.D. in agriculture and over 12 years of experience working in Next-Generation Sequencing. He has a deep background in genomics and bioinformatics with a specialization in applying predictive analytics across a varied set of genomics problems in life sciences. Dr. Devisetty is currently working as a senior data science manager at Greenlight Biosciences, where he leads a team of bioinformatics scientists and data scientists to support the various bioinformatics and data science projects at GreenLight Biosciences with a mission to create mRNA-based solutions that can provide a cleaner environment and healthier people.

Note from the author:*

You can use the resources provided in this GitHub repo as you work through the hands-on activities includes in each chapter of the book. This repo is laid out with resources matched to each chapter of the book - such as the JSON used to define IAM policies, sample files, relevant links, etc.

Download a free PDF

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781800560413