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

Tuning the models

One common problem in model development is overfitting. Overfitting happens when the model performs well on the training data but does not generalize well on unseen data. There are several reasons for overfitting, such as high model complexity, training for many epochs, too little training data, and so on. Model tuning is the processing of increasing model performance by limiting model complexity, regularization, dropout, and so on to reduce overfitting. This is generally done in DL by optimizing “hyperparameters”.

Before we further discuss tuning models, let’s understand the difference between parameters and hyperparameters. Parameters are inputs to the ML library or model that can be generally learned from the model. Some examples of the parameters of NNs include weights and biases. During model training, through backpropagation, the model learns those parameters, whereas hyperparameters are those parameters that cannot be learned from the...