Deep Learning in Bioinformatics
Techniques and Applications in Practice
2nd Edition
Will be published approx. on 1. July 2026
Book
Paperback/Softback
450 pages
978-0-443-44629-0 (ISBN)
Description
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.
Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.
More details
Edition
2nd edition
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Weight
450 gr
ISBN-13
978-0-443-44629-0 (9780443446290)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
Book
01/2022
Academic Press
€160.50
Shipment within 15-20 days
Person
Dr. Habib Izadkhah is an Associate Professor at the Department of Computer Science, University of Tabriz, Iran. He worked in the industry for a decade as a software engineer before becoming an academic. His research interests include algorithms and graphs, software engineering, and bioinformatics. More recently he has been working on the developing and applying Deep Learning to a variety of problems, dealing with biomedical images, speech recognition, text understanding, and generative models. He has contributed to various research projects, authored a number of research papers in international conferences, workshops, and journals, and also has written five books, including Source Code Modularization: Theory and Techniques from Springer.
Content
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