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BL
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| Record Number
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851447
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| Main Entry
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Ramsundar, Bharath.
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| Title & Author
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Deep Learning for the Life Sciences : : Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More /\ Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande.
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| Edition Statement
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First edition.
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| Publication Statement
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Sebastopol, CA :: O'Reilly Media,, 2019.
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| Page. NO
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1 online resource
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| ISBN
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1492039802
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: 9781492039808
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1492039837
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9781492039839
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| Notes
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Includes index.
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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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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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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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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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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
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| Subject
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Artificial intelligence.
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Life sciences-- Data processing.
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Machine learning.
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Artificial intelligence.
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Life sciences-- Data processing.
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Machine learning.
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NATURE-- Reference.
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SCIENCE-- Life Sciences-- Biology.
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| Subject
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SCIENCE-- Life Sciences-- General.
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| Dewey Classification
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570
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| LC Classification
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QH307.2
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| Added Entry
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Eastman, Peter.
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Pande, Vijay.
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Walters, Patrick.
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