Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

by Bharath Ramsundar, Peter Eastman, Patrick Walters

Deep learning has already achieved remarkable results in many fields. Now it's making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields.

Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You'll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine--an example that represents one of science's greatest challenges.


Learn the basics of performing machine learning on molecular data
Understand why deep learning is a powerful tool for genetics and genomics
Apply deep learning to understand biophysical systems
Get a brief introduction to machine learning with DeepChem
Use deep learning to analyze microscopic images
Analyze medical scans using deep learning techniques
Learn about variational autoencoders and generative adversarial networks
Interpret what your model is doing and how it's working

Genres: Science, Technical, Textbooks, Artificial Intelligence

236 pages, ebook

Published April 10, 2019

Reviews

[James Warren · Rating 3 out of 5]

This book has a distinctive "color by numbers" feel. It provides highly useful background information, but the provided examples have limited illustrative value. Reading this book is a nice overview, but it certainly will not prepare you to conduct similar analyses independently.

[Will Morgan · Rating 3 out of 5]

While this is an interesting intro to multiple areas of deep learning across bioinformatics, cheminformatics, and genomics, it has some flaws in that deepchem, the python package this book revolves around, needs some very specific versions of python, tensorflow, scikit learn, and more. It's common to be overwhelmed with warning labels about deprecations, or simply things not working.

While yes, it is possible to make a separate environment and run the book exercises there, it does raise complications for using deepchem with other packages and as part of a wider project.

I wonder if this package is still fully maintained, or if any parts have been abandoned, as some functionalities gone through in the book simply do not exist, even when the docs site is searched. This book makes deepchem seem exciting and full of potential until the point where you try to use it.

[Miguel Madero · Rating 4 out of 5]

Useful intro, it covers a very wide range of applications (most of life-sciences) and techniques (a good subset of Deep Learning). It has a high-level explanation of both the field and the potential DL techniques that could be used, the current state (benchmarks and real-world uses), limitations, and potential.

Chapter 1, 2, and 3 are good intros to both LifeSciences, DeepLearning, and Deechem (a library used throughout the book). Then each chapter focuses on a different area of LifeSciences. ML for Molecules is a great example of good high-level coverage.

Minor critiques: The last few chapters felt a bit rushed. The code samples could use some work and maybe a better setup with Jupiter notebooks or something similar so it's easier to follow along.

[Elmer Peramo · Rating 2 out of 5]

This is more like a survey paper than a cohesive textbook. There are some interesting chapters, but others are lacking in depth and detail. It's better for experienced deep learning practitioners who want to learn domains in chemistry, biophysics, and genomics than for biologists who need to study deep learning.

[Richard · Rating 5 out of 5]

I was looking for a summary of how life science data is represented in machine learning experiments. This is perfect for what I needed.

[Radu Angelescu · Rating 3 out of 5]

Nice

It's a nice book. I would have liked it to be more in depth but I guess it's meant to be for casual readers.

[Josua Naiborhu · Rating 4 out of 5]

interesting book to read for anyone who is interested in deep learning for the life sciences in the introductory level.