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Deep Learning for Genomics

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

View More author details

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

Why subscribe?

17. Other Books You May Enjoy

Part 2 – Deep Learning for Genomic Applications

The primary goal of this part is to transition you from machine learning to deep learning and introduce you to different deep learning technologies specific to genomic applications, using real-life examples to transform raw genomics data into biological insights.

This section comprises the following chapters:

Chapter 4, Deep Learning for Genomics
Chapter 5, Introducing Convolutional Neural Networks for Genomics
Chapter 6, Recurrent Neural Networks in Genomics
Chapter 7, Unsupervised Deep Learning with Autoencoders
Chapter 8, GANs for Improving Models in Genomics

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Authors (1)

Upendra Kumar Devisetty

Upendra Kumar Devisetty

Dr. Upendra Kumar Devisetty is a Senior Data Science Manager at Greenlight Biosciences where he leads a team of Bioinformatics Scientists, Data Scientists, Business Intelligence Analysts, and Project managers to support the various Bioinformatics and Data Science projects at GreenLight Biosciences and contribute to a cleaner environment, healthier people, and a stronger food supply chain.

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