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

What is unsupervised DL?

Unless you are lucky, the chances are that most of the data that comes to you is unlabeled, whether it is images on the web, text from a document, gene expression data from NGS experiments, and so on. Even if they come labeled, they are not clean and perfect datasets. This is where UL algorithms are useful. In UL, the algorithm is presented with the training datasets without any label, which means these datasets don’t have a particular outcome or specific instructions on what to do with them. The job of the UL model is to automatically extract features from unlabeled datasets and use those features to find hidden patterns. The unsupervised models first try to extract simple features from the data, then stitch them together to form more advanced features, and finally, come up with an outcome. Unlike SL models, these models don’t have a ground truth to evaluate the performance of the models using metrics such as accuracy, mean squared error (MSE...