Deep Learning in Bioinformatics

Techniques and Applications in Practice

1st Edition - January 8, 2022
Author: Habib Izadkhah
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 8 2 2 - 6
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 8 3 6 - 3

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 import… Read more

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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.

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

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Habib Izadkhah