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Deep Learning in Bioinformatics: Techniques and Applications in Practice

Autor Habib Izadkhah
en Limba Engleză Paperback – 19 ian 2022
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
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Specificații

ISBN-13: 9780128238226
ISBN-10: 0128238224
Pagini: 380
Dimensiuni: 191 x 235 mm
Greutate: 0.65 kg
Editura: ELSEVIER SCIENCE

Public țintă

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

Cuprins

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