Deep Learning in Bioinformatics: Techniques and Applications in Practice
Autor Habib Izadkhahen Limba Engleză Paperback – iul 2026
- Introduces deep learning in an easy-to-understand way
- Presents how deep learning can be utilized for addressing many important problems in bioinformatics
- Presents the state-of-the-art algorithms in deep learning and bioinformatics
- Introduces deep learning libraries in bioinformatics
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Specificații
ISBN-13: 9780443446290
ISBN-10: 0443446296
Pagini: 450
Dimensiuni: 191 x 235 mm
Ediția:2
Editura: ELSEVIER SCIENCE
ISBN-10: 0443446296
Pagini: 450
Dimensiuni: 191 x 235 mm
Ediția:2
Editura: ELSEVIER SCIENCE
Cuprins
1. Why Life Science?
2. A Review of Machine Learning
3. An Introduction to the Python Ecosystem for Deep Learning
4. Preprocessing Techniques for Bioinformatics Data
5. Foundations of Neural Networks and Deep Learning
6. Convolutional Neural Networks in Biology and Bioinformatics
7. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification
8. Sequence-Based Analysis and Neural Networks
9. Graph Neural Networks for Bioinformatics
10. Transfer Learning in Bioinformatics: Adapting Pre-Trained Models
11. Pathway-Based Neural Networks for Biological Insights
12. Multi-Omics Integration Using Multi-Input Neural Networks
13. Deep Learning for Genomic and Metabolomics Data Analysis
14. Autoencoders and Deep Generative Models in Bioinformatics
15. Interpretable Neural Networks for Understanding Decisions in Biological Processes
16. Applications of Deep Learning in Personalized Medicine
17. Ethical Considerations and Challenges in Deep Learning for Bioinformatics
2. A Review of Machine Learning
3. An Introduction to the Python Ecosystem for Deep Learning
4. Preprocessing Techniques for Bioinformatics Data
5. Foundations of Neural Networks and Deep Learning
6. Convolutional Neural Networks in Biology and Bioinformatics
7. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification
8. Sequence-Based Analysis and Neural Networks
9. Graph Neural Networks for Bioinformatics
10. Transfer Learning in Bioinformatics: Adapting Pre-Trained Models
11. Pathway-Based Neural Networks for Biological Insights
12. Multi-Omics Integration Using Multi-Input Neural Networks
13. Deep Learning for Genomic and Metabolomics Data Analysis
14. Autoencoders and Deep Generative Models in Bioinformatics
15. Interpretable Neural Networks for Understanding Decisions in Biological Processes
16. Applications of Deep Learning in Personalized Medicine
17. Ethical Considerations and Challenges in Deep Learning for Bioinformatics