
Deep Learning in Bioinformatics
Techniques and Applications in Practice
Habib Izadkhah(Author)
Academic Press
Published on 19. January 2022
Book
Paperback/Softback
380 pages
978-0-12-823822-6 (ISBN)
Shipment within 15-20 days
Description
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Students, educators, and researchers in the field of bioinformatics, machine learning, biomedical engineering, applied statistics, biostatistics, and computer science Secondary market/audience: Research scientists in medical and biological sciences
Product notice
Paperback (trade)
Dimensions
Height: 231 mm
Width: 187 mm
Thickness: 19 mm
Weight
780 gr
ISBN-13
978-0-12-823822-6 (9780128238226)
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
New editions

Book
approx. 07/2026
2nd Edition
Academic Press
€175.50
Not yet published
Additional editions

E-Book
01/2022
Academic Press
€148.00
Available for download
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.
Author
Associate Professor, Department of Computer Science, University of Tabriz, Tabriz, Iran
Content
1. Why Life Science?
2. A Review of Machine Learning
3. An Introduction of Python Ecosystem for Deep Learning
4. Basic Structure of Neural Networks
5. Training Multi-Layer Neural Networks
6. Classification in Bioinformatics
7. Introduction to Deep learning
8. Medical Image Processing: An Insight to Convolutional Neural Networks
9. Popular Deep Learning Image Classifiers
10. Electrocardiogram (ECG) Arrhythmia Classification
11. Autoencoders and Deep Generative Models in Bioinformatics
12. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification
13. Application, Challenge, and Suggestion
2. A Review of Machine Learning
3. An Introduction of Python Ecosystem for Deep Learning
4. Basic Structure of Neural Networks
5. Training Multi-Layer Neural Networks
6. Classification in Bioinformatics
7. Introduction to Deep learning
8. Medical Image Processing: An Insight to Convolutional Neural Networks
9. Popular Deep Learning Image Classifiers
10. Electrocardiogram (ECG) Arrhythmia Classification
11. Autoencoders and Deep Generative Models in Bioinformatics
12. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification
13. Application, Challenge, and Suggestion