Deep Learning for the Life Sciences. Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

Spis treści ebooka

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly 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?
      • Covalent bonds
      • Noncovalent 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
      • Hydrogen bonds
      • Salt bridges
      • Pi-stacking interactions
      • Fingerprints
      • Some implementation details
    • 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
      • Fluorophores and fluorescent tags
    • Sample Preparation Artifacts
      • The Mesosome: An Imaginary Organelle
  • Deep Learning Applications
    • Cell Counting
      • Implementing cell counting in DeepChem
    • Cell Segmentation
      • Implementing cell segmentation in DeepChem
    • 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 Models 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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