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Deep learning for the life sciences : applying deep learning to genomics, microscopy, drug discovery, and more

Deep learning for the life sciences : applying deep learning to genomics, microscopy, drug discovery, and more (4회 대출)

자료유형
단행본
개인저자
Ramsundar, Bharath.
서명 / 저자사항
Deep learning for the life sciences : applying deep learning to genomics, microscopy, drug discovery, and more / Bharath Ramsundar ... [et al.].
발행사항
Sebastopol :   O'Reilly Media,   2019.  
형태사항
x, 222 p. : ill. ; 24 cm.
ISBN
9781492039839 (hbk.)
서지주기
Includes bibliographical references and index.
일반주제명
Deep learning.
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020 ▼a 9781492039839 (hbk.)
035 ▼a (KERIS)BIB000015259018
040 ▼a 221031 ▼c 221031 ▼d 211009
082 0 4 ▼a 006.31 ▼2 23
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090 ▼a 006.31 ▼b D311
245 0 0 ▼a Deep learning for the life sciences : ▼b applying deep learning to genomics, microscopy, drug discovery, and more / ▼c Bharath Ramsundar ... [et al.].
260 ▼a Sebastopol : ▼b O'Reilly Media, ▼c 2019.
300 ▼a x, 222 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Deep learning.
700 1 ▼a Ramsundar, Bharath.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 D311 등록번호 121249392 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

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 -- 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 -- 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 -- Protein Structures -- Protein 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 -- 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 -- 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 -- MRI Scans -- Learning 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 -- Interpretability, Explainability, and Real-World Consequences -- Conclusion -- Chapter 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 -- Chapter 12. Prospects and Perspectives -- Medical Diagnosis -- Personalized Medicine -- Pharmaceutical Development -- Biology Research -- Conclusion -- Index -- About the Authors -- Colophon -- .

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