Deep Learning in Bioinformatics

Nyhet

Techniques and Applications in Practice

Häftad, Engelska, 2026

Av Habib Izadkhah

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Deep Learning in Bioinformatics: Techniques and Applications in Practice, Second Edition 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. The Second Edition includes several new chapters, and the applications and examples have been updated for new Deep Learning advances and techniques throughout. 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 many important problems in bioinformatics
Presents the state-of-the-art algorithms in deep learning and bioinformatics
Introduces deep learning libraries in bioinformatics

Produktinformation

Utgivningsdatum2026-07-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor450
Upplaga2
FörlagElsevier Science
ISBN9780443446290

Tillhör följande kategorier

Biomedicinsk teknik inom Medicin
Biokemisk teknik inom Naturvetenskap och teknik
Programmeringsböcker inom Data och it
Artificiell intelligens inom Data och it
Systemvetenskap och AI inom Data och it

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.

1. Why Life Science?2. A Review of Machine Learning3. An Introduction to the Python Ecosystem for Deep Learning4. Preprocessing Techniques for Bioinformatics Data5. Foundations of Neural Networks and Deep Learning6. Convolutional Neural Networks in Biology and Bioinformatics7. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification8. Sequence-Based Analysis and Neural Networks9. Graph Neural Networks for Bioinformatics10. Transfer Learning in Bioinformatics: Adapting Pre-Trained Models11. Pathway-Based Neural Networks for Biological Insights12. Multi-Omics Integration Using Multi-Input Neural Networks13. Deep Learning for Genomic and Metabolomics Data Analysis14. Autoencoders and Deep Generative Models in Bioinformatics15. Interpretable Neural Networks for Understanding Decisions in Biological Processes16. Applications of Deep Learning in Personalized Medicine17. Ethical Considerations and Challenges in Deep Learning for Bioinformatics