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)
Part 1 – Machine Learning in Genomics
Part 2 – Deep Learning for Genomic Applications
Part 3 – Operationalizing models

Challenges working with genomics datasets

Genomics is the study of the genetic constitution of a whole organism, which are instructions for an organism to build and grow. It is now routinely possible to sequence a whole genome of organisms, thanks to next-generation sequencing (NGS) technologies. Despite easy access to genome sequencing technology, the primary challenge is the availability of these genomic datasets at scale because of technical limitations, cost, difficulty collecting more data, and so on. It is well known in the DL community that in general, the more data that DL can have access to, the more accurate the predictions are.

Not having enough data restricts the utility of the available data and limits building highly accurate DL models with it. Here are some of the problems arising from small data:

  1. Small data poses problems with model training and the use of trained models in real-world applications because it is prone to overfitting problems.
  2. Small data...