Book Image

Deep Learning for Genomics

By : Upendra Kumar Devisetty
Book Image

Deep Learning for Genomics

By: Upendra Kumar Devisetty

Overview of this book

Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you’ll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you’ll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.
Table of Contents (18 chapters)
1
Part 1 – Machine Learning in Genomics
5
Part 2 – Deep Learning for Genomic Applications
11
Part 3 – Operationalizing models

How can GANs help improve models?

DL requires a lot of data to mine insights and make an informed decision. The success of DL to generalize well is mainly attributed to the training of NN architectures on large amounts of data. However, it is not always possible to acquire more data because of several reasons, as explained earlier. What if we can generate synthetic data that is modeled around real-world data so that we can augment the limited datasets and improve our model predictions? Synthetic data has a multitude of use cases in DL because of the infinite variations of synthetic data that can be produced. DL is the primary beneficiary of synthetic data, and research shows that enhancing real-world data with synthetic data produced using generative models such as GANs can significantly improve model fitness and thereby result in better predictions. GANs can help improve models directly and indirectly through the generation of synthetic data, which can make sensitive data accessible...