Nyhet

Deep Learning in Bioinformatics

Techniques and Applications in Practice

AvHabib Izadkhah

Häftad, Engelska, 2026

1 791 kr

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Beskrivning

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.

Introduces deep learning in an easy-to-understand way
Presents how deep learning can be utilized for addressing many important problems in bioinformatics
Provides state-of-the-art algorithms in deep learning and bioinformatics
Introduces deep learning libraries in bioinformatics

Produktinformation

Utgivningsdatum:2026-07-01
Mått:191 x 235 x undefined mm
Vikt:450 g
Format:Häftad
Språk:Engelska
Antal sidor:450
Upplaga:2
Förlag:Elsevier Science
ISBN:9780443446290

Utforska kategorier

Biomedicinsk teknik inom Medicin
Biokemisk teknik inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Mer om författaren

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.

Innehållsförteckning

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