Deep Learning for the Life Sciences 1st edition by Bharath Ramsundar, Peter Eastman, Pat Walters, Vijay Pande ISBN 1492039839 9781492039839

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ISBN 10: 1492039839
ISBN 13: 9781492039839
Author: Bharath Ramsundar, Peter Eastman, Pat Walters, Vijay Pande

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

Deep Learning for the Life Sciences 1st Table of contents:

1. Why Life Science?

Why Deep Learning?

Contemporary Life Science Is About Data

What Will You Learn?

2. Introduction to Deep Learning

Linear Models

Multilayer Perceptrons

Training Models

Validation

Regularization

Hyperparameter Optimization

Other Types of Models

Convolutional Neural Networks

Recurrent Neural Networks

Further Reading

3. Machine Learning with DeepChem

DeepChem Datasets

Training a Model to Predict Toxicity of Molecules

Case Study: Training an MNIST Model

The MNIST Digit Recognition Dataset

A Convolutional Architecture for MNIST

Conclusion

4. Machine Learning for Molecules

What Is a Molecule?

What Are Molecular Bonds?

Molecular Graphs

Molecular Conformations

Chirality of Molecules

Featurizing a Molecule

SMILES Strings and RDKit

Extended-Connectivity Fingerprints

Molecular Descriptors

Graph Convolutions

Training a Model to Predict Solubility

MoleculeNet

SMARTS Strings

Conclusion

5. Biophysical Machine Learning

Protein Structures

Protein Sequences

A Short Primer on Protein Binding

Biophysical Featurizations

Grid Featurization

Atomic Featurization

The PDBBind Case Study

PDBBind Dataset

Featurizing the PDBBind Dataset

Conclusion

6. Deep Learning for Genomics

DNA, RNA, and Proteins

And Now for the Real World

Transcription Factor Binding

A Convolutional Model for TF Binding

Chromatin Accessibility

RNA Interference

Conclusion

7. Machine Learning for Microscopy

A Brief Introduction to Microscopy

Modern Optical Microscopy

The Diffraction Limit

Electron and Atomic Force Microscopy

Super-Resolution Microscopy

Deep Learning and the Diffraction Limit?

Preparing Biological Samples for Microscopy

Staining

Sample Fixation

Sectioning Samples

Fluorescence Microscopy

Sample Preparation Artifacts

Deep Learning Applications

Cell Counting

Cell Segmentation

Computational Assays

Conclusion

8. Deep Learning for Medicine

Computer-Aided Diagnostics

Probabilistic Diagnoses with Bayesian Networks

Electronic Health Record Data

The Dangers of Large Patient EHR Databases?

Deep Radiology

X-Ray Scans and CT Scans

Histology

MRI Scans

Learning Models as Therapeutics

Diabetic Retinopathy

Conclusion

Ethical Considerations

Job Losses

Summary

9. Generative Models

Variational Autoencoders

Generative Adversarial Networks

Applications of Generative Models in the Life Sciences

Generating New Ideas for Lead Compounds

Protein Design

A Tool for Scientific Discovery

The Future of Generative Modeling

Working with Generative Models

Analyzing the Generative Model’s Output

Conclusion

10. Interpretation of Deep Models

Explaining Predictions

Optimizing Inputs

Predicting Uncertainty

Interpretability, Explainability, and Real-World Consequences

Conclusion

11. A Virtual Screening Workflow Example

Preparing a Dataset for Predictive Modeling

Training a Predictive Model

Preparing a Dataset for Model Prediction

Applying a Predictive Model

Conclusion

12. Prospects and Perspectives

Medical Diagnosis

Personalized Medicine

Pharmaceutical Development

Biology Research

Conclusion

Index

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Tags: Bharath Ramsundar, Peter Eastman, Pat Walters, Vijay Pande, Deep Learning, Life Sciences