Deep Learning in Bioinformatics

Deep Learning in Bioinformatics

Techniques and Applications in Practice

1st Edition - January 8, 2022

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  • Author: Habib Izadkhah
  • Paperback ISBN: 9780128238226
  • eBook ISBN: 9780128238363

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Description

Deep Learning in Bioinformatics: Techniques and Applications in Practice introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.

Key Features

  • Introduces deep learning in an easy-to-understand way
  • Presents how deep learning can be utilized for addressing some important problems in bioinformatics
  • Presents the state-of-the-art algorithms in deep learning and bioinformatics
  • Introduces deep learning libraries in bioinformatics

Readership

Students, educators, and researchers in the field of bioinformatics, machine learning, biomedical engineering, applied statistics, biostatistics, and computer science. Research scientists in medical and biological sciences

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • Acknowledgments
  • Preface
  • Chapter 1: Why life science?
  • Abstract
  • 1.1. Introduction
  • 1.2. Why deep learning?
  • 1.3. Contemporary life science is about data
  • 1.4. Deep learning and bioinformatics
  • 1.5. What will you learn?
  • Chapter 2: A review of machine learning
  • Abstract
  • 2.1. Introduction
  • 2.2. What is machine learning?
  • 2.3. Challenge with machine learning
  • 2.4. Overfitting and underfitting
  • 2.5. Types of machine learning
  • 2.6. The math behind deep learning
  • 2.7. TensorFlow and Keras
  • 2.8. Real-world tensors
  • 2.9. Summary
  • Chapter 3: An introduction of Python ecosystem for deep learning
  • Abstract
  • 3.1. Basic setup
  • 3.2. SciPy (scientific Python) ecosystem
  • 3.3. Scikit-learn
  • 3.4. A quick refresher in Python
  • 3.5. NumPy
  • 3.6. Matplotlib crash course
  • 3.7. Pandas
  • 3.8. How to load dataset
  • 3.9. Dimensions of your data
  • 3.10. Correlations between features
  • 3.11. Techniques to understand each feature in the dataset
  • 3.12. Prepare your data for deep learning
  • 3.13. Feature selection for machine learning
  • 3.14. Split dataset into training and testing sets
  • 3.15. Summary
  • Chapter 4: Basic structure of neural networks
  • Abstract
  • 4.1. Introduction
  • 4.2. The neuron
  • 4.3. Layers of neural networks
  • 4.4. How a neural network is trained?
  • 4.5. Delta learning rule
  • 4.6. Generalized delta rule
  • 4.7. Gradient descent
  • 4.8. Example: delta rule
  • 4.9. Limitations of single-layer neural networks
  • 4.10. Summary
  • Chapter 5: Training multilayer neural networks
  • Abstract
  • 5.1. Introduction
  • 5.2. Backpropagation algorithm
  • 5.3. Momentum
  • 5.4. Neural network models in keras
  • 5.5. ‘Hello world!’ of deep learning
  • 5.6. Tuning hyperparameters
  • 5.7. Data preprocessing
  • 5.8. Summary
  • Chapter 6: Classification in bioinformatics
  • Abstract
  • 6.1. Introduction
  • 6.2. Multiclass classification
  • 6.3. Summary
  • Chapter 7: Introduction to deep learning
  • Abstract
  • 7.1. Introduction
  • 7.2. Improving the performance of deep neural networks
  • 7.3. Configuring the learning rate in keras
  • 7.4. Imbalanced dataset
  • 7.5. Breast cancer detection
  • 7.6. Molecular classification of cancer by gene expression
  • 7.7. Summary
  • Chapter 8: Medical image processing: an insight to convolutional neural networks
  • Abstract
  • 8.1. Convolutional neural network architecture
  • 8.2. Convolution layer
  • 8.3. Pooling layer
  • 8.4. Stride and padding
  • 8.5. Convolutional layer in keras
  • 8.6. Coronavirus (COVID-19) disease diagnosis
  • 8.7. Predicting breast cancer
  • 8.8. Diabetic retinopathy detection
  • 8.9. Summary
  • Chapter 9: Popular deep learning image classifiers
  • Abstract
  • 9.1. Introduction
  • 9.2. LeNet-5
  • 9.3. AlexNet
  • 9.4. ZFNet
  • 9.5. VGGNet
  • 9.6. GoogLeNet/inception
  • 9.7. ResNet
  • 9.8. DenseNet
  • 9.9. SE-Net
  • 9.10. Summary
  • Chapter 10: Electrocardiogram (ECG) arrhythmia classification
  • Abstract
  • 10.1. Introduction
  • 10.2. MIT-BIH arrhythmia database
  • 10.3. Preprocessing
  • 10.4. Data augmentation
  • 10.5. Architecture of the CNN model
  • 10.6. Summary
  • Chapter 11: Autoencoders and deep generative models in bioinformatics
  • Abstract
  • 11.1. Introduction
  • 11.2. Autoencoders
  • 11.3. Variant types of autoencoders
  • 11.4. An example of denoising autoencoders – bone suppression in chest radiographs
  • 11.5. Implementation of autoencoders for chest X-ray images (pneumonia)
  • 11.6. Generative adversarial network
  • 11.7. Convolutional generative adversarial network
  • 11.8. Summary
  • Chapter 12: Recurrent neural networks: generating new molecules and proteins sequence classification
  • Abstract
  • 12.1. Introduction
  • 12.2. Types of recurrent neural network
  • 12.3. The problem, short-term memory
  • 12.4. Bidirectional LSTM
  • 12.5. Generating new molecules
  • 12.6. Protein sequence classification
  • 12.7. Summary
  • Chapter 13: Application, challenge, and suggestion
  • Abstract
  • 13.1. Introduction
  • 13.2. Legendary deep learning architectures, CNN, and RNN
  • 13.3. Deep learning applications in bioinformatics
  • 13.4. Biological networks
  • 13.5. Perspectives, limitations, and suggestions
  • 13.6. DeepChem, a powerful library for bioinformatics
  • 13.7. Summary
  • Index

Product details

  • No. of pages: 380
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: January 8, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780128238226
  • eBook ISBN: 9780128238363

About the Author

Habib Izadkhah

Dr. Habib Izadkhah is an Associate Professor at the Department of Computer Science, University of Tabriz, Iran. He worked in the industry for a decade as a software engineer before becoming an academic. His research interests include algorithms and graphs, software engineering, and bioinformatics. More recently he has been working on the developing and applying Deep Learning to a variety of problems, dealing with biomedical images, speech recognition, text understanding, and generative models. He has contributed to various research projects, authored a number of research papers in international conferences, workshops, and journals, and also has written five books, including Source Code Modularization: Theory and Techniques from Springer.

Affiliations and Expertise

Associate Professor, Department of Computer Science, University of Tabriz, Tabriz, Iran

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