Deep Learning in Bioinformatics: Techniques and Applications in Practice
- Length: 380 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2022-02-02
- ISBN-10: 0128238224
- ISBN-13: 9780128238226
- Sales Rank: #2771528 (See Top 100 Books)
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.
Front Cover Deep Learning in Bioinformatics Copyright Contents Acknowledgments Preface 1 Why life science? 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? 2 A review of machine learning 2.1 Introduction 2.2 What is machine learning? 2.3 Challenge with machine learning 2.4 Overfitting and underfitting 2.4.1 Mitigating overfitting 2.4.2 Adjusting parameters using cross-validation 2.4.3 Cross-validation methods 2.5 Types of machine learning 2.5.1 Supervised learning 2.5.2 Unsupervised learning 2.5.3 Reinforcement learning 2.6 The math behind deep learning 2.6.1 Tensors 2.6.2 Relevant mathematical operations 2.6.3 The math behind machine learning: statistics 2.7 TensorFlow and Keras 2.8 Real-world tensors 2.9 Summary 3 An introduction of Python ecosystem for deep learning 3.1 Basic setup 3.2 SciPy (scientific Python) ecosystem 3.3 Scikit-learn 3.4 A quick refresher in Python 3.4.1 Identifier 3.4.2 Comments 3.4.3 Data type 3.4.4 Control flow statements 3.4.5 Data structure 3.4.6 Functions 3.5 NumPy 3.6 Matplotlib crash course 3.7 Pandas 3.8 How to load dataset 3.8.1 Considerations when loading CSV data 3.8.2 Pima Indians diabetes dataset 3.8.3 Loading CSV files in NumPy 3.8.4 Loading CSV files in Pandas 3.9 Dimensions of your data 3.10 Correlations between features 3.11 Techniques to understand each feature in the dataset 3.11.1 Histograms 3.11.2 Box-and-whisker plots 3.11.3 Correlation matrix plot 3.12 Prepare your data for deep learning 3.12.1 Scaling features to a range 3.12.2 Data normalizing 3.12.3 Binarize data (make binary) 3.13 Feature selection for machine learning 3.13.1 Univariate selection 3.13.2 Recursive feature elimination 3.13.3 Principal component analysis 3.13.4 Feature importance 3.14 Split dataset into training and testing sets 3.15 Summary 4 Basic structure of neural networks 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 4.7.1 Stochastic gradient descent 4.7.2 Batch gradient descent 4.7.3 Mini-batch gradient descent 4.8 Example: delta rule 4.8.1 Implementation of the SGD method 4.8.2 Implementation of the batch method 4.9 Limitations of single-layer neural networks 4.10 Summary 5 Training multilayer neural networks 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 5.7.1 Vectorization 5.7.2 Value normalization 5.8 Summary 6 Classification in bioinformatics 6.1 Introduction 6.1.1 Binary classification 6.1.2 Pima indians onset of diabetes dataset 6.1.2.1 Import libraries 6.1.2.2 Load data 6.1.2.3 Keras model 6.1.2.4 Compile the model 6.1.2.5 Fit the model 6.1.2.6 Evaluate the model 6.1.2.7 Tie it all together 6.1.2.8 Make predictions 6.1.3 Label encoding 6.2 Multiclass classification 6.2.1 Sigmoid and softmax activation functions 6.2.2 Types of classification 6.3 Summary 7 Introduction to deep learning 7.1 Introduction 7.2 Improving the performance of deep neural networks 7.2.1 Vanishing gradient 7.2.2 Overfitting 7.2.2.1 Reducing the network's size 7.2.2.2 Dropout 7.2.2.3 Weight regularization 7.2.3 Computational load 7.3 Configuring the learning rate in keras 7.3.1 Adaptive learning rate 7.3.2 Layer weight initializers 7.4 Imbalanced dataset 7.5 Breast cancer detection 7.5.1 Goals 7.5.2 Introduction and task definition 7.5.3 Implementation 7.5.3.1 Loading, preprocessing, preparations for modeling 7.5.3.2 Fully connected neural network (FCNN) 7.5.3.3 Adding dropout to the network (FCNN + dropout) 7.5.3.4 Adding L2 weight regularization (FCNN + L2) 7.5.3.5 Adding L2 weight regularization and dropout (FCNN + L2 + dropout) 7.5.3.6 Adding L1_L2 weight regularization (FCNN + L1_L2) 7.5.3.7 Reducing the size of the network 7.5.3.8 Summary 7.6 Molecular classification of cancer by gene expression 7.6.1 Goals 7.6.2 Introduction and task definition 7.6.3 Implementation 7.6.3.1 Loading, preprocessing, preparations for modeling 7.6.3.2 Dimension reduction using principal component analysis (PCA) 7.6.3.3 Model 7.7 Summary 8 Medical image processing: an insight to convolutional neural networks 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 8.6.1 Goals 8.6.2 Introduction and task definition 8.6.3 Implementation 8.6.3.1 Importing required libraries 8.6.3.2 Plotting some instances of the dataset 8.6.3.3 Defining the model 8.6.3.4 Discussing the relevance of deep learning for small-data problems 8.6.3.5 Predicting covid-19 8.6.4 Conclusion 8.7 Predicting breast cancer 8.7.1 Goals 8.7.2 Introduction and task definition 8.7.3 Implementation 8.7.3.1 Importing required libraries 8.7.3.2 Looking for all available directories in Kaggle account 8.7.3.3 Plotting images using cv2 module 8.7.3.4 Finding specific pattern in the name of images 8.7.3.5 Preprocessing data 8.7.3.6 Dealing with imbalanced data 8.7.3.7 Defining the sequential model 8.7.4 Conclusion 8.8 Diabetic retinopathy detection 8.8.1 Goals 8.8.2 Introduction and task definition 8.8.3 Implementation 8.8.3.1 Importing required libraries and reading the data 8.8.3.2 Preprocessing data 8.8.3.3 Defining model based on functional API 8.8.3.4 Defining another model using ResNet50 model 8.8.4 Conclusion 8.9 Summary 9 Popular deep learning image classifiers 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 10 Electrocardiogram (ECG) arrhythmia classification 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 11 Autoencoders and deep generative models in bioinformatics 11.1 Introduction 11.2 Autoencoders 11.2.1 Encoder 11.2.2 Decoder 11.2.3 Distance function 11.3 Variant types of autoencoders 11.3.1 Undercomplete autoencoders 11.3.2 Deep autoencoders 11.3.3 Convolutional autoencoders 11.3.4 Sparse autoencoders 11.3.5 Denoising autoencoders 11.3.6 Variational autoencoders Intuition VAE is a generative model How does a variational autoencoder work? Creating decoder Building the architecture of the VAE: connecting the encoder and decoder Defining loss function and compiling model 11.3.7 Contractive autoencoders 11.4 An example of denoising autoencoders – bone suppression in chest radiographs 11.4.1 Architecture 11.5 Implementation of autoencoders for chest X-ray images (pneumonia) 11.5.1 Undercompleted autoencoder 11.5.2 Sparse autoencoder 11.5.3 Denoising autoencoder 11.5.4 Variational autoencoder 11.5.5 Contractive autoencoder 11.6 Generative adversarial network 11.6.1 GAN network architecture 11.6.2 GAN network cost function 11.6.3 Cost function optimization process in GAN 11.6.4 General GAN training process 11.7 Convolutional generative adversarial network 11.7.1 Deconvolution layer 11.7.2 DCGAN network structure 11.8 Summary 12 Recurrent neural networks: generating new molecules and proteins sequence classification 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 12.5.1 Simplified molecular-input line-entry system 12.5.2 A generative model for molecules 12.5.3 Generating new SMILES 12.5.4 Analyzing the generative model's output 12.6 Protein sequence classification 12.6.1 Protein structure 12.6.2 Protein function 12.6.3 Prediction of protein function 12.6.4 LSTM with dropout 12.6.5 LSTM with bidirectional and CNN 12.7 Summary 13 Application, challenge, and suggestion 13.1 Introduction 13.2 Legendary deep learning architectures, CNN, and RNN 13.3 Deep learning applications in bioinformatics 13.4 Biological networks 13.4.1 Learning tasks on graphs 13.4.2 Graph neural networks 13.5 Perspectives, limitations, and suggestions 13.6 DeepChem, a powerful library for bioinformatics 13.7 Summary Index Back Cover
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