Ramsundar Bharath, Eastman Peter, Walters Patrick, Pande Vijay. Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

O’Reilly, 2019. — 253 p. — ISBN13: 978-1-492-03983-9Deep 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 workingWhy Deep Learning?
Contemporary Life Science Is About DataWhat Will You Learn?Intro to Deep Learning
Linear Models
Multilayer Perceptrons
Training Models
Regularization
Hyperparameter Optimization
Other Types of Models
Further Reading
Machine Learning with DeepChem
DeepChem Datasets
Training a Model to Predict Toxicity of Molecules
Case Study: Training an MNIST ModelMachine Learning for Molecules
What Is a Molecule?
Featurizing a Molecule
Graph Convolutions
Training a Model to Predict Solubility
MoleculeNetBiophysical Machine Learning
Protein Structures
Biophysical Featurizations
The PDBBind Case StudyDNA, RNA, and Proteins
And Now for the Real World
Transcription Factor Binding
Chromatin Accessibility
RNA InterferenceMachine Learning for Microscopy
A Brief Introduction to Microscopy
The Diffractio Limit
Preparing Biological Samples for Microscopy
Deep Learning ApplicationsComputer-Aided Diagnostics
Probabilistic Diagnoses with Bayesian Networks
Electronic Health Record Data
Deep Radiology
Learning Models as Therapeutics
Diabetic RetinopathyVariational Autoencoders
Generative Adversarial Networks
Applications of Generative Models in the Life Sciences
Working with Generative ModelsExplaining Predictions
Optimizing Inputs
Predicting Uncertainty
Interpretability, Explainability, and Real-World ConsequencesVirtual Screening Workflow Example
Preparing a Dataset for Predictive Modeling
Training a Predictive Model
Preparing a Dataset for Model Prediction
Applying a Predictive ModelMedical Diagnosis
Personalized Medicine
Pharmaceutical Development
Biology Research