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.
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Table of Contents
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
