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|a Ramsundar, Bharath.
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|a Deep Learning for the Life Sciences :
|b Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More /
|c Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande.
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|a First edition.
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|c 2019.
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|a Includes index.
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|a Online resource; title from PDF title page (EBSCO, April 16, 2019).
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|a Cover; Copyright; Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Chapter 1. Why Life Science?; Why Deep Learning?; Contemporary Life Science Is About Data; What Will You Learn?; Chapter 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; Chapter 3. Machine Learning with DeepChem; DeepChem Datasets
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| 505 |
8 |
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|a Training a Model to Predict Toxicity of MoleculesCase Study: Training an MNIST Model; The MNIST Digit Recognition Dataset; A Convolutional Architecture for MNIST; Conclusion; Chapter 4. Machine Learning for Molecules; What Is a Molecule?; What Are Molecular 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; Chapter 5. Biophysical Machine Learning
|
| 505 |
8 |
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|a Protein StructuresProtein Sequences; A Short Primer on Protein Binding; Biophysical Featurizations; Grid Featurization; Atomic Featurization; The PDBBind Case Study; PDBBind Dataset; Featurizing the PDBBind Dataset; Conclusion; Chapter 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; Chapter 7. Machine Learning for Microscopy; A Brief Introduction to Microscopy; Modern Optical Microscopy; The Diffraction Limit
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| 505 |
8 |
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|a Electron and Atomic Force MicroscopySuper-Resolution Microscopy; Deep Learning and the Diffraction Limit?; Preparing Biological Samples for Microscopy; Staining; Sample Fixation; Sectioning Samples; Fluorescence Microscopy; Sample Preparation Artifacts; Deep Learning Applications; Cell Counting; Cell Segmentation; Computational Assays; Conclusion; Chapter 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
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| 505 |
8 |
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|a MRI ScansLearning Models as Therapeutics; Diabetic Retinopathy; Conclusion; Ethical Considerations; Job Losses; Summary; Chapter 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 Model's Output; Conclusion; Chapter 10. Interpretation of Deep Models; Explaining Predictions; Optimizing Inputs; Predicting Uncertainty
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| 504 |
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|a Includes bibliographical references and index.
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|a Life sciences
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|i Print version:
|a Ramsundar, Bharath.
|t Deep Learning for the Life Sciences.
|b First edition.
|d Sebastopol, CA : O'Reilly Media, 2019
|z 1492039837
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