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

ebook

Autor: Bharath Ramsundar, Peter Eastman, Patrick Walters
ISBN: 9781492039785
stron: 238, Format: ebook
Data wydania: 2019-04-10
Księgarnia: Helion

Cena książki: 254,15 zł (poprzednio: 299,00 zł)
Oszczędzasz: 15% (-44,85 zł)

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Tagi: Inne - Programowanie

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

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Spis treści

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

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