Contents......Page 3
Preface......Page 7
Why Deep Learning?......Page 9
Contemporary Life Science Is About Data......Page 10
What Will You Learn?......Page 11
Intro to Deep Learning......Page 15
Linear Models......Page 16
Multilayer Perceptrons......Page 18
Training Models......Page 21
Regularization......Page 23
Hyperparameter Optimization......Page 25
Other Types of Models......Page 26
Further Reading......Page 29
Machine Learning with DeepChem......Page 30
DeepChem Datasets......Page 31
Training a Model to Predict Toxicity of Molecules......Page 32
Case Study: Training an MNIST Model......Page 39
Conclusion......Page 46
Machine Learning for Molecules......Page 47
What Is a Molecule?......Page 48
Featurizing a Molecule......Page 55
Graph Convolutions......Page 57
Training a Model to Predict Solubility......Page 58
MoleculeNet......Page 60
Conclusion......Page 63
Biophysical Machine Learning......Page 64
Protein Structures......Page 66
Biophysical Featurizations......Page 72
The PDBBind Case Study......Page 78
Conclusion......Page 86
DNA, RNA, and Proteins......Page 89
And Now for the Real World......Page 91
Transcription Factor Binding......Page 94
Chromatin Accessibility......Page 97
RNA Interference......Page 100
Conclusion......Page 103
Machine Learning for Microscopy......Page 104
A Brief Introduction to Microscopy......Page 106
The Diffractio Limit......Page 110
Preparing Biological Samples for Microscopy......Page 115
Deep Learning Applications......Page 121
Conclusion......Page 129
Computer-Aided Diagnostics......Page 131
Probabilistic Diagnoses with Bayesian Networks......Page 133
Electronic Health Record Data......Page 134
Deep Radiology......Page 138
Learning Models as Therapeutics......Page 145
Diabetic Retinopathy......Page 146
Conclusion......Page 149
Variational Autoencoders......Page 152
Generative Adversarial Networks......Page 154
Applications of Generative Models in the Life Sciences......Page 155
Working with Generative Models......Page 158
Conclusion......Page 162
Explaining Predictions......Page 165
Optimizing Inputs......Page 169
Predicting Uncertainty......Page 172
Interpretability, Explainability, and Real-World Consequences......Page 176
Conclusion......Page 177
Virtual Screening Workflow Example......Page 178
Preparing a Dataset for Predictive Modeling......Page 179
Training a Predictive Model......Page 185
Preparing a Dataset for Model Prediction......Page 190
Applying a Predictive Model......Page 194
Conclusion......Page 201
Medical Diagnosis......Page 202
Personalized Medicine......Page 204
Pharmaceutical Development......Page 205
Biology Research......Page 207
Conclusion......Page 208
Index......Page 209