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

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

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Deep learning has already achieved remarkable results in many fields. Now its 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. Youll 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 sciences 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 its working

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Deep Learning for the Life Sciences by Bharath Ramsundar Peter Eastman - photo 1
Deep Learning for the Life Sciences

by Bharath Ramsundar , Peter Eastman , Patrick Walters , and Vijay Pande

Copyright 2019 Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

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  • April 2019: First Edition
Revision History for the First Edition
  • 2019-03-27: First Release

See http://bit.ly/deep-learning-life-science for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Deep Learning for the Life Sciences, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

The views expressed in this work are those of the authors, and do not represent the publishers views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-492-03983-9

[LSI]

Preface

In recent years, life science and data science have converged. Advances in robotics and automation have enabled chemists and biologists to generate enormous amounts of data. Scientists today are capable of generating more data in a day than their predecessors 20 years ago could have generated in an entire career. This ability to rapidly generate data has also created a number of new scientific challenges. We are no longer in an era where data can be processed by loading it into a spreadsheet and making a couple of graphs. In order to distill scientific knowledge from these datasets, we must be able to identify and extract nonobvious relationships.

One technique that has emerged over the last few years as a powerful tool for identifying patterns and relationships in data is deep learning, a class of algorithms that have revolutionized approaches to problems such as image analysis, language translation, and speech recognition. Deep learning algorithms excel at identifying and exploiting patterns in large datasets. For these reasons, deep learning has broad applications across life science disciplines. This book provides an overview of how deep learning has been applied in a number of areas including genetics, drug discovery, and medical diagnosis. Many of the examples we describe are accompanied by code examples that provide a practical introduction to the methods and give the reader a starting point for future research and exploration.

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Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/deepchem/DeepLearningLifeSciences.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Deep Learning for the Life Sciences by Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande (OReilly). Copyright 2019 Bharath Ramsundar, Karl Leswing, Peter Eastman, and Vijay Pande, 978-1-492-03983-9.

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