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

Autoencoders for genomics

Several applications of autoencoders for genomics exist. The most common type of application, however, is for predicting gene expression from microarray and RNA-Seq datasets. Let’s understand how autoencoders work for gene expression analysis.

Gene expression

The main application of autoencoders, as you learned in the previous section, is for gene expression analysis, which includes

  • Time-series gene expression where they are mainly used at the preprocessing step for clustering, cDNA microarrays
  • RNA-Seq, where they are used to predict the organization of transcriptomics machinery
  • Gene expression, where they are mainly used for identification of biological signals and patterns respectively

In a typical gene expression experiment, the inputs are typically numerical values estimating how much RNA is produced from a DNA template through transcription across various cells, tissues, or conditions. Let’s look at some popular...