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by Vijay Pande, Patrick Walters, Peter Eastman, Bharath Ramsundar
Deep Learning for the Life Sciences
Preface
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
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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