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.
1. Chapter 3 An introduction of Python ecosystem for deep learning - p. 31 2. Chapter 4 Basic structure of neural networks - p. 67 3. Chapter 5 Training multilayer neural networks - p. 95 4. Chapter 6 Classification in bioinformatics - p. 113 5. Chapter 7 Introduction to deep learning - p. 131 6. Chapter 8 Medical image processing: an insight to convolutional neural networks - p. 175 7. Chapter 9 Popular deep learning image classifiers - p. 215 8. Chapter 10 Electrocardiogram (ECG) arrhythmia classification - p. 249 9. Chapter 11 Autoencoders and deep generative models in bioinformatics - p. 261 10. Chapter 12 Recurrent neural networks: generating new molecules and proteins sequence classification - p. 321 11. Chapter 13 Application, challenge, and suggestion - p. 347 12. Index - p. 359
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.
1. Chapter 3 An introduction of Python ecosystem for deep learning - p. 31 2. Chapter 4 Basic structure of neural networks - p. 67 3. Chapter 5 Training multilayer neural networks - p. 95 4. Chapter 6 Classification in bioinformatics - p. 113 5. Chapter 7 Introduction to deep learning - p. 131 6. Chapter 8 Medical image processing: an insight to convolutional neural networks - p. 175 7. Chapter 9 Popular deep learning image classifiers - p. 215 8. Chapter 10 Electrocardiogram (ECG) arrhythmia classification - p. 249 9. Chapter 11 Autoencoders and deep generative models in bioinformatics - p. 261 10. Chapter 12 Recurrent neural networks: generating new molecules and proteins sequence classification - p. 321 11. Chapter 13 Application, challenge, and suggestion - p. 347 12. Index - p. 359
