Deep Learning in bioinformatics: techniques and applications in practice
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 p...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
William Andrew Publishing
2022
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | 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.- 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 |
| Beschreibung: | xv, 363 Seiten Illustrationen, Diagramme 235 mm. |
| ISBN: | 9780128238226 |
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Contents Acknowledgments . Preface. xiii xv Why life science? . 1 Introduction. Why deep learning?. Contemporary life science is about data. Deep learning and bioinformatics. What will you learn?. 1 1 3 4 4 A review of machine learning. 9 Introduction. What is machine learning?. Challenge with machine learning. Overfitting and underfitting . 2.4.1 Mitigating
overfitting . 2.4.2 Adjusting parameters using cross-validation. 2.4.3 Cross-validation methods . Types of machine learning. 2.5.1 Supervised learning . 2.5.2 Unsupervised learning . 2.5.3 Reinforcement learning. The math behind deep learning . 2.6.1 Tensors. 2.6.2 Relevant mathematical operations. 2.6.3 The math behind machinelearning: statistics. TensorFlow and Keras. Real-world tensors. Summary. 9 9 13 14 15 15 16 17 18 20 21 22 22 24 25 27 28 29 An introduction of Python ecosystem for deep
learning. 31 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 Datatype . 3.4.4 Control flow statements . 3.4.5 Data structure. 3.4.6 Functions. 31 32 32 32 33 33 33 35 37 38 CHAPTER 1 1.1 1.2 1.3 1.4 1.5 CHAPTER 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 CHAPTER 3 3.1 vii
viii Contents NumPy . Matplotlib crash course . Pandas. 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. 38 44 45 46 46 46 47 49 3.5 3.6 3.7 3.8 50 52 53 53 54 54 57 58 58 59 60 60 61 62 64 64 66 Basic structure of neural networks . 67 67 69 71 76 4.10 Introduction . The neuron
. Layers of neural networks . How a neural network is trained?. Delta learning rule. Generalized delta rule . Gradient descent . 4.7.1 Stochastic gradient descent. 4.7.2 Batch gradient descent . 4.7.3 Mini-batch gradient descent . Example: delta rule . 4.8.1 Implementation of the SGD method . 4.8.2 Implementation of the batch method. Limitations of single-layerneural networks . Summary. CHAPTER 5 5.1 Training multilayer neuralnetworks
. 95 Introduction . 95 CHAPTER 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 76 79 80 80 81 83 85 86 89 90 92
Contents 5.2 5.3 5.4 5.5 5.6 5.7 5.8 CHAPTER 6 6.1 6.2 6.3 CHAPTER 7 ix Backpropagation algorithm . Momentum . Neural network models in keras. ‘Hello world!’ of deep learning . Tuning hyperparameters. Data preprocessing . 5.7.1 Vectorization . 5.7.2 Value normalization . Summary. 96 99 100 103 108 109 109 109 110 Classification in bioinformatics. 113 Introduction. 6.1.1 Binary classification. 6.1.2 Pima indians onset of diabetes dataset. 6.1.3 Label
encoding. Multiclass classification. 6.2.1 Sigmoid and softmax activation functions. 6.2.2 Types of classification. Summary. 113 113 115 124 125 128 130 130 Introduction to deep learning . 131 Introduction. 131 Improving the performance of deep neural networks . 132 7.2.1 Vanishing gradient . 132 7.2.2 Overfitting . 133 7.2.3 Computational load . 150 7.3 Configuring the learning rate in keras. 151 7.3.1 Adaptive learning rate. 152 7.3.2 Layer weight initializers. 153
7.4 Imbalanced dataset . 153 7.5 Breast cancer detection . 155 7.5.1 Goals. 155 7.5.2 Introduction and task definition. 155 7.5.3 Implementation. 156 7.6 Molecular classification of cancer by gene expression . 163 7.6.1 Goals. 163 7.6.2 Introduction and task definition. 163 7.6.3 Implementation. 164 7.7 Summary. 173 7.1 7.2 CHAPTER 8 8.1 8.2 8.3 8.4 8.5 Medical image processing: an insight to convolutional neural networks. 175 Convolutional neural network architecture. Convolution layer . Pooling
layer. Stride and padding. Convolutional layer in keras. 175 176 179 180 183
x Contents Coronavirus (COVID-19)disease diagnosis. 8.6.1 Goals. 8.6.2 Introduction and task definition. 8.6.3 Implementation. 8.6.4 Conclusion. 8.7 Predicting breast cancer. 8.7.1 Goals. 8.7.2 Introduction and task definition. 8.7.3 Implementation. 8.7.4 Conclusion. 8.8 Diabetic retinopathy detection. 8.8.1 Goals. 8.8.2 Introduction and task definition. 8.8.3 Implementation. 8.8.4
Conclusion. 8.9 Summary. 184 184 184 184 191 192 192 192 193 202 202 202 202 203 210 211 Popular deep learning image classifiers. 215 Introduction . LeNet-5. AlexNet. ZFNet. VGGNet. GoogLeNet/inception . ResNet . DenseNet. SE-Net .
Summary. 215 216 217 220 222 228 237 242 245 247 CHAPTER 10 Electrocardiogram (ECG) arrhythmiaclassification. 249 10.1 Introduction. 10.5 Architecture of the CNN model. 10.6 Summary. 249 250 250 255 256 258 Autoencoders and deep generative models in bioinformatics. 261 Introduction . 261 263 265 265 265 266 266 268 8.6 CHAPTER 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 10.2 MIT-ВІН arrhythmia database. 10.3 Preprocessing . 10.4 Data augmentation. CHAPTER 11 11.1 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.
Contents 11.4 11.5 11.6 11.7 11.8 CHAPTER 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 xi 11.3.3 Convolutional autoencoders . 11.3.4 Sparse autoencoders. 11.3.5 Denoising autoencoders . 11.3.6 Variational autoencoders. 11.3.7 Contractive autoencoders . An example of denoising autoencoders - bone suppression in chest radiographs 11.4.1 Architecture. 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. 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 . Convolutional generative adversarial network. 11.7.1 Deconvolution layer. 11.7.2 DCGAN network structure. Summary. 269 270 272 274 283 284 288 290 293 295 296 298 306 308 308 310 310 310 314 314 314 318 Recurrent neural networks: generating new molecules and proteins sequence classification. 321 Introduction. Types of recurrent neural network . The problem, short-term memory. Bidirectional LSTM. 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. 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. Summary. 321 322 323 325 328 329 330 336 337 337 339 339 339 344 344 346 CHAPTER 13 Application, challenge, and suggestion. 13.1 Introduction. 13.2 Legendary deep learning architectures,
CNN, and RNN. 347 347 347
xii Contents 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. 348 351 351 352 353 357 357 Index . 359 |
| adam_txt |
Contents Acknowledgments . Preface. xiii xv Why life science? . 1 Introduction. Why deep learning?. Contemporary life science is about data. Deep learning and bioinformatics. What will you learn?. 1 1 3 4 4 A review of machine learning. 9 Introduction. What is machine learning?. Challenge with machine learning. Overfitting and underfitting . 2.4.1 Mitigating
overfitting . 2.4.2 Adjusting parameters using cross-validation. 2.4.3 Cross-validation methods . Types of machine learning. 2.5.1 Supervised learning . 2.5.2 Unsupervised learning . 2.5.3 Reinforcement learning. The math behind deep learning . 2.6.1 Tensors. 2.6.2 Relevant mathematical operations. 2.6.3 The math behind machinelearning: statistics. TensorFlow and Keras. Real-world tensors. Summary. 9 9 13 14 15 15 16 17 18 20 21 22 22 24 25 27 28 29 An introduction of Python ecosystem for deep
learning. 31 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 Datatype . 3.4.4 Control flow statements . 3.4.5 Data structure. 3.4.6 Functions. 31 32 32 32 33 33 33 35 37 38 CHAPTER 1 1.1 1.2 1.3 1.4 1.5 CHAPTER 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 CHAPTER 3 3.1 vii
viii Contents NumPy . Matplotlib crash course . Pandas. 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. 38 44 45 46 46 46 47 49 3.5 3.6 3.7 3.8 50 52 53 53 54 54 57 58 58 59 60 60 61 62 64 64 66 Basic structure of neural networks . 67 67 69 71 76 4.10 Introduction . The neuron
. Layers of neural networks . How a neural network is trained?. Delta learning rule. Generalized delta rule . Gradient descent . 4.7.1 Stochastic gradient descent. 4.7.2 Batch gradient descent . 4.7.3 Mini-batch gradient descent . Example: delta rule . 4.8.1 Implementation of the SGD method . 4.8.2 Implementation of the batch method. Limitations of single-layerneural networks . Summary. CHAPTER 5 5.1 Training multilayer neuralnetworks
. 95 Introduction . 95 CHAPTER 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 76 79 80 80 81 83 85 86 89 90 92
Contents 5.2 5.3 5.4 5.5 5.6 5.7 5.8 CHAPTER 6 6.1 6.2 6.3 CHAPTER 7 ix Backpropagation algorithm . Momentum . Neural network models in keras. ‘Hello world!’ of deep learning . Tuning hyperparameters. Data preprocessing . 5.7.1 Vectorization . 5.7.2 Value normalization . Summary. 96 99 100 103 108 109 109 109 110 Classification in bioinformatics. 113 Introduction. 6.1.1 Binary classification. 6.1.2 Pima indians onset of diabetes dataset. 6.1.3 Label
encoding. Multiclass classification. 6.2.1 Sigmoid and softmax activation functions. 6.2.2 Types of classification. Summary. 113 113 115 124 125 128 130 130 Introduction to deep learning . 131 Introduction. 131 Improving the performance of deep neural networks . 132 7.2.1 Vanishing gradient . 132 7.2.2 Overfitting . 133 7.2.3 Computational load . 150 7.3 Configuring the learning rate in keras. 151 7.3.1 Adaptive learning rate. 152 7.3.2 Layer weight initializers. 153
7.4 Imbalanced dataset . 153 7.5 Breast cancer detection . 155 7.5.1 Goals. 155 7.5.2 Introduction and task definition. 155 7.5.3 Implementation. 156 7.6 Molecular classification of cancer by gene expression . 163 7.6.1 Goals. 163 7.6.2 Introduction and task definition. 163 7.6.3 Implementation. 164 7.7 Summary. 173 7.1 7.2 CHAPTER 8 8.1 8.2 8.3 8.4 8.5 Medical image processing: an insight to convolutional neural networks. 175 Convolutional neural network architecture. Convolution layer . Pooling
layer. Stride and padding. Convolutional layer in keras. 175 176 179 180 183
x Contents Coronavirus (COVID-19)disease diagnosis. 8.6.1 Goals. 8.6.2 Introduction and task definition. 8.6.3 Implementation. 8.6.4 Conclusion. 8.7 Predicting breast cancer. 8.7.1 Goals. 8.7.2 Introduction and task definition. 8.7.3 Implementation. 8.7.4 Conclusion. 8.8 Diabetic retinopathy detection. 8.8.1 Goals. 8.8.2 Introduction and task definition. 8.8.3 Implementation. 8.8.4
Conclusion. 8.9 Summary. 184 184 184 184 191 192 192 192 193 202 202 202 202 203 210 211 Popular deep learning image classifiers. 215 Introduction . LeNet-5. AlexNet. ZFNet. VGGNet. GoogLeNet/inception . ResNet . DenseNet. SE-Net .
Summary. 215 216 217 220 222 228 237 242 245 247 CHAPTER 10 Electrocardiogram (ECG) arrhythmiaclassification. 249 10.1 Introduction. 10.5 Architecture of the CNN model. 10.6 Summary. 249 250 250 255 256 258 Autoencoders and deep generative models in bioinformatics. 261 Introduction . 261 263 265 265 265 266 266 268 8.6 CHAPTER 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 10.2 MIT-ВІН arrhythmia database. 10.3 Preprocessing . 10.4 Data augmentation. CHAPTER 11 11.1 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.
Contents 11.4 11.5 11.6 11.7 11.8 CHAPTER 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 xi 11.3.3 Convolutional autoencoders . 11.3.4 Sparse autoencoders. 11.3.5 Denoising autoencoders . 11.3.6 Variational autoencoders. 11.3.7 Contractive autoencoders . An example of denoising autoencoders - bone suppression in chest radiographs 11.4.1 Architecture. 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. 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 . Convolutional generative adversarial network. 11.7.1 Deconvolution layer. 11.7.2 DCGAN network structure. Summary. 269 270 272 274 283 284 288 290 293 295 296 298 306 308 308 310 310 310 314 314 314 318 Recurrent neural networks: generating new molecules and proteins sequence classification. 321 Introduction. Types of recurrent neural network . The problem, short-term memory. Bidirectional LSTM. 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. 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. Summary. 321 322 323 325 328 329 330 336 337 337 339 339 339 344 344 346 CHAPTER 13 Application, challenge, and suggestion. 13.1 Introduction. 13.2 Legendary deep learning architectures,
CNN, and RNN. 347 347 347
xii Contents 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. 348 351 351 352 353 357 357 Index . 359 |
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| index_date | 2024-07-03T21:15:56Z |
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| publisher | William Andrew Publishing |
| record_format | marc |
| spelling | Izadkhah, Habib Verfasser (DE-588)1232844055 aut Deep Learning in bioinformatics techniques and applications in practice London William Andrew Publishing 2022 xv, 363 Seiten Illustrationen, Diagramme 235 mm. txt rdacontent n rdamedia nc rdacarrier 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.- 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 Deep Learning (DE-588)1135597375 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe Medizintechnik, Biomedizintechnik Deep Learning (DE-588)1135597375 s Bioinformatik (DE-588)4611085-9 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034005746&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Izadkhah, Habib Deep Learning in bioinformatics techniques and applications in practice Deep Learning (DE-588)1135597375 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4611085-9 |
| title | Deep Learning in bioinformatics techniques and applications in practice |
| title_auth | Deep Learning in bioinformatics techniques and applications in practice |
| title_exact_search | Deep Learning in bioinformatics techniques and applications in practice |
| title_exact_search_txtP | Deep Learning in bioinformatics techniques and applications in practice |
| title_full | Deep Learning in bioinformatics techniques and applications in practice |
| title_fullStr | Deep Learning in bioinformatics techniques and applications in practice |
| title_full_unstemmed | Deep Learning in bioinformatics techniques and applications in practice |
| title_short | Deep Learning in bioinformatics |
| title_sort | deep learning in bioinformatics techniques and applications in practice |
| title_sub | techniques and applications in practice |
| topic | Deep Learning (DE-588)1135597375 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Deep Learning Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034005746&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT izadkhahhabib deeplearninginbioinformaticstechniquesandapplicationsinpractice |