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

Developing models

Model development is my favorite part of the DL life cycle (and probably 90% of data scientists’ favorite part too). The goal of model development is to build a model with minimum loss over training and validation datasets to prevent overfitting, which is done by searching for the parameters that best fit the model. A typical model development phase involves four different steps, as shown in the following diagram:

Figure 9.4 – Different steps of the DL model building phase

Let’s discuss each of the four steps briefly:

  1. Selecting an appropriate algorithm: In this step, an appropriate DL algorithm is selected based on the problem that you are solving.
  2. Model training: Once an algorithm is selected, the DL algorithm is provided with the training data, loss function, random hyperparameters, and objective metrics to optimize using backpropagation.
  3. Model tuning: The model initially has random hyperparameters that...