Deep Learning in Bioinformatics: Techniques and Applications in Practice, Second Edition explores how deep learning 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. This updated edition includes several new chapters, applications, and examples for new Deep Learning advances and techniques.
Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.
1. 2: An introduction of Python ecosystem for deep learning - p. 27 2. 3: Introduction to neural networks - p. 65 3. 4: Introduction to deep learning - p. 119 4. 5: Medical image processing: an insight to convolutional neural networks - p. 165 5. 6: Convolutional neural networks in bioinformatics and biology - p. 211 6. 7: Recurrent neural networks: generating new molecules and proteins sequence classification - p. 237 7. 8: Deep learning in sequence analysis for bioinformatics and biology - p. 267 8. 9: Autoencoders and deep generative models in bioinformatics - p. 307 9. 10: Graph neural networks for bioinformatics - p. 357 10. 11: Deep learning for genomic and metabolomics data analysis - p. 375 11. 12: Deep learning in microbiome studies - p. 437 12. 13: Multi-modal neural networks for multi-omics - p. 465 13. 14: Gene interaction analysis and cancer prediction - p. 497 14. 15: Transfer learning in bioinformatics: adapting pre-trained models - p. 513 15. 16: Interpretable neural networks for understanding decisions in biological processes - p. 529 16. 17: Challenges of deep learning in bioinformatics and biology - p. 543 17. Index - p. 561
Deep Learning in Bioinformatics: Techniques and Applications in Practice, Second Edition explores how deep learning 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. This updated edition includes several new chapters, applications, and examples for new Deep Learning advances and techniques.
Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.
1. 2: An introduction of Python ecosystem for deep learning - p. 27 2. 3: Introduction to neural networks - p. 65 3. 4: Introduction to deep learning - p. 119 4. 5: Medical image processing: an insight to convolutional neural networks - p. 165 5. 6: Convolutional neural networks in bioinformatics and biology - p. 211 6. 7: Recurrent neural networks: generating new molecules and proteins sequence classification - p. 237 7. 8: Deep learning in sequence analysis for bioinformatics and biology - p. 267 8. 9: Autoencoders and deep generative models in bioinformatics - p. 307 9. 10: Graph neural networks for bioinformatics - p. 357 10. 11: Deep learning for genomic and metabolomics data analysis - p. 375 11. 12: Deep learning in microbiome studies - p. 437 12. 13: Multi-modal neural networks for multi-omics - p. 465 13. 14: Gene interaction analysis and cancer prediction - p. 497 14. 15: Transfer learning in bioinformatics: adapting pre-trained models - p. 513 15. 16: Interpretable neural networks for understanding decisions in biological processes - p. 529 16. 17: Challenges of deep learning in bioinformatics and biology - p. 543 17. Index - p. 561
