Deep Learning for the Life Sciences
Language: English
Published by O'Reilly Media, US, 2019
- Softcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
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Add to basketItem description from seller
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.…
Seller Inventory # LU-9781492039839
- Title
- Deep Learning for the Life Sciences
- Author
- Vijay Pande, Peter Eastman, Bharath Ramsundar, Karl Leswing
- Publisher
- O'Reilly Media, US
- Publication year
- 2019
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1492039837
- ISBN 13
- 9781492039839
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
"Synopsis" may belong to another edition of this title.
About the Author
Bharath Ramsundar received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He recently finished his PhD in computer science at Stanford University (all but dissertation) with the Pande group, supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences.
Peter Eastman develops software for computational chemistry and biology in the Bioengineering Department at Stanford University.
Pat Walters heads the Computation & Informatics group at Relay Therapeutics. His group focuses on novel applications of computational methods that drive drug discovery.
Vijay Pande, PhD is a general partner at Andreessen Horowitz where he leads the firm's investments in companies at the cross section of biology and computer science including areas such as the application of computation, Machine Learning, and Artificial Intelligence broadly into Biology and Healthcare as well as the application of novel transformative scientific advances. He is also an Adjunct Professor of Bioengineering at Stanford, where he advises research at the intersection of Computer Science and Biology, pioneering computational methods and their application to medicine and biology, resulting in over 200 publications, two patents and two novel drug treatments.
As an entrepreneur at the convergence of biology and computer science, Vijay is the founder of the Folding@Home Distributed Computing Project for disease research that pushes the boundaries of the development and application of computer science techniques (such as distributed systems, machine learning, and exotic computer architectures) into biology and medicine, in both fundamental research as well as the development of new therapeutics. Also during his time at Stanford, Vijay co-founded Globavir Biosciences, where he translated his research advances at Stanford and Folding@Home into a successful startup, discovering cures for Dengue Fever and Ebola. In his teens, he was the first employee at video game startup Naughty Dog Software, maker of Crash Bandicoot.
"About the title" may belong to another edition of this title.
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