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210123 ||| eng |
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|a 9781492039808
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| 050 |
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4 |
|a QH307.2
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| 100 |
1 |
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|a Ramsundar, Bharath
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| 245 |
0 |
0 |
|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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| 250 |
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|a First edition
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| 260 |
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|a Sebastopol, CA
|b O'Reilly Media
|c 2019
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| 300 |
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|a 1 online resource
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| 505 |
0 |
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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
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| 505 |
0 |
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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 |
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|a Includes bibliographical references and index
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| 505 |
0 |
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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 |
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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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| 505 |
0 |
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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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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a SCIENCE / Life Sciences / General / bisacsh
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| 653 |
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|a Artificial intelligence / fast
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| 653 |
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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| 653 |
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|a NATURE / Reference / bisacsh
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Life sciences / Data processing / fast
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| 653 |
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|a SCIENCE / Life Sciences / Biology / bisacsh
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| 653 |
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|a Intelligence artificielle
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a artificial intelligence / aat
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| 653 |
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|a Life sciences / Data processing
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| 653 |
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|a Sciences de la vie / Informatique
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| 653 |
|
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|a Machine Learning
|
| 700 |
1 |
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|a Eastman, Peter
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| 700 |
1 |
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|a Walters, Patrick
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| 700 |
1 |
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|a Pande, Vijay
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b OREILLY
|a O'Reilly
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| 500 |
|
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|a Includes index
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| 015 |
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|a GBB978634
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| 776 |
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|z 9781492039839
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| 776 |
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|z 1492039802
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| 776 |
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|z 1492039837
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| 776 |
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|z 9781492039808
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781492039822/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 570
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| 082 |
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|a 500
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