Deep Learning in Bioinformatics
Techniques and Applications in Practice
- 1st Edition - January 8, 2022
- Imprint: Academic Press
- Author: Habib Izadkhah
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 2 2 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 3 6 - 3
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 import… Read more
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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.
- Introduces deep learning in an easy-to-understand way
- Presents how deep learning can be utilized for addressing some important problems in bioinformatics
- Presents the state-of-the-art algorithms in deep learning and bioinformatics
- Introduces deep learning libraries in bioinformatics
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
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
- Edition: 1
- Published: January 8, 2022
- Imprint: Academic Press
- Language: English
HI
Habib Izadkhah
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.
Affiliations and expertise
Associate Professor, Department of Computer Science, University of Tabriz, Tabriz, IranRead Deep Learning in Bioinformatics on ScienceDirect