You're reading from Deep Learning for Genomics Data-driven approaches for genomics applications in life sciences and biotechnology

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781804615447

Length 270 pages

Edition 1st Edition

Concepts

Deep Learning

Author (1):

Upendra Kumar Devisetty

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Table of Contents (18) Chapters

Preface

1. Part 1 – Machine Learning in Genomics

2. Chapter 1: Introducing Machine Learning for Genomics FREE CHAPTER

3. Chapter 2: Genomics Data Analysis

4. Chapter 3: Machine Learning Methods for Genomic Applications

5. Part 2 – Deep Learning for Genomic Applications

6. Chapter 4: Deep Learning for Genomics

7. Chapter 5: Introducing Convolutional Neural Networks for Genomics

8. Chapter 6: Recurrent Neural Networks in Genomics

9. Chapter 7: Unsupervised Deep Learning with Autoencoders

10. Chapter 8: GANs for Improving Models in Genomics

11. Part 3 – Operationalizing models

12. Chapter 9: Building and Tuning Deep Learning Models

13. Chapter 10: Model Interpretability in Genomics

14. Chapter 11: Model Deployment and Monitoring

15. Chapter 12: Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics

16. Index

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Use case – Model interpretability for genomics

In this hands-on exercise section, we will build a similar convolutional NN (CNN) model that we built in Chapter 9, Building and Tuning Deep Learning Models, but unlike in Chapter 9, here we will use a simulated dataset of DNA sequences of length 50 bases (whereas in Chapter 9, we have DNA sequence of length 101 bases). In addition, the binding sites in this example are not just for Transcription Factors (TFs) but any protein. The labels are designated as 0 and 1, corresponding to positive and negative binding sites (0 = no binding site and 1 = binding site).

The goal of this is to train a CNN model to predict the DNA binding site of the protein and visualize it in the predictions. Since these are artificial sequences, we have injected the AAAGAGGAAGTT motif into the positive sequence, but don’t worry—the CNN doesn’t know that.