Deep Learning for the Life Sciences

Table of contents

1. Preface
   1. Conventions Used in This Book
   2. Using Code Examples
   3. O’Reilly Online Learning
   4. How to Contact Us
   5. Acknowledgments
2. 1. Why Life Science?
   1. Why Deep Learning?
   2. Contemporary Life Science Is About Data
   3. What Will You Learn?
3. 2. Introduction to Deep Learning
   1. Linear Models
   2. Multilayer Perceptrons
   3. Training Models
   4. Validation
   5. Regularization
   6. Hyperparameter Optimization
   7. Other Types of Models
      1. Convolutional Neural Networks
      2. Recurrent Neural Networks
   8. Further Reading
4. 3. Machine Learning with DeepChem
   1. DeepChem Datasets
   2. Training a Model to Predict Toxicity of Molecules
   3. Case Study: Training an MNIST Model
      1. The MNIST Digit Recognition Dataset
      2. A Convolutional Architecture for MNIST
   4. Conclusion
5. 4. Machine Learning for Molecules
   1. What Is a Molecule?
      1. What Are Molecular Bonds?
      2. Molecular Graphs
      3. Molecular Conformations
      4. Chirality of Molecules
   2. Featurizing a Molecule
      1. SMILES Strings and RDKit
      2. Extended-Connectivity Fingerprints
      3. Molecular Descriptors
   3. Graph Convolutions
   4. Training a Model to Predict Solubility
   5. MoleculeNet
      1. SMARTS Strings
   6. Conclusion
6. 5. Biophysical Machine Learning
   1. Protein Structures
      1. Protein Sequences
      2. A Short Primer on Protein Binding
   2. Biophysical Featurizations
      1. Grid Featurization
      2. Atomic Featurization
   3. The PDBBind Case Study
      1. PDBBind Dataset
      2. Featurizing the PDBBind Dataset
   4. Conclusion
7. 6. Deep Learning for Genomics
   1. DNA, RNA, and Proteins
   2. And Now for the Real World
   3. Transcription Factor Binding
      1. A Convolutional Model for TF Binding
   4. Chromatin Accessibility
   5. RNA Interference
   6. Conclusion
8. 7. Machine Learning for Microscopy
   1. A Brief Introduction to Microscopy
      1. Modern Optical Microscopy
   2. The Diffraction Limit
      1. Electron and Atomic Force Microscopy
      2. Super-Resolution Microscopy
      3. Deep Learning and the Diffraction Limit?
   3. Preparing Biological Samples for Microscopy
      1. Staining
      2. Sample Fixation
      3. Sectioning Samples
      4. Fluorescence Microscopy
      5. Sample Preparation Artifacts
   4. Deep Learning Applications
      1. Cell Counting
      2. Cell Segmentation
      3. Computational Assays
   5. Conclusion
9. 8. Deep Learning for Medicine
   1. Computer-Aided Diagnostics
   2. Probabilistic Diagnoses with Bayesian Networks
   3. Electronic Health Record Data
      1. The Dangers of Large Patient EHR Databases?
   4. Deep Radiology
      1. X-Ray Scans and CT Scans
      2. Histology
      3. MRI Scans
   5. Learning Models as Therapeutics
   6. Diabetic Retinopathy
   7. Conclusion
      1. Ethical Considerations
      2. Job Losses
      3. Summary
10. 9. Generative Models
    1. Variational Autoencoders
    2. Generative Adversarial Networks
    3. Applications of Generative Models in the Life Sciences
       1. Generating New Ideas for Lead Compounds
       2. Protein Design
       3. A Tool for Scientific Discovery
       4. The Future of Generative Modeling
    4. Working with Generative Models
       1. Analyzing the Generative Model’s Output
    5. Conclusion
11. 10. Interpretation of Deep Models
    1. Explaining Predictions
    2. Optimizing Inputs
    3. Predicting Uncertainty
    4. Interpretability, Explainability, and Real-World Consequences
    5. Conclusion
12. 11. A Virtual Screening Workflow Example
    1. Preparing a Dataset for Predictive Modeling
    2. Training a Predictive Model
    3. Preparing a Dataset for Model Prediction
    4. Applying a Predictive Model
    5. Conclusion
13. 12. Prospects and Perspectives
    1. Medical Diagnosis
    2. Personalized Medicine
    3. Pharmaceutical Development
    4. Biology Research
    5. Conclusion
14. Index