Deep Learning for the Life Sciences. Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More (e-book) Katowice

Deep learning has already achieved remarkable results in many fields. Now its 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. Youll 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 sciences 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 its working 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