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

ML for genomics

Thanks to rapid advancements in NGS, genomics has shown tremendous growth in the last decade, which has led to an outpouring of massive sequence data. In addition to whole-genome sequencing (WGS), other promising techniques have emerged, such as whole-exome sequencing (WES) to measure the expressed region of the genome, whole-transcriptome sequencing (WTS) or RNA-sequencing (RNA-seq) to measure mRNA expression, ChIP-sequencing (ChIP-seq) to identify transcription-factor binding sites, and Ribo-sequencing (Ribo-seq) to identify actively translating mRNAs for quantifying relative protein abundance, and so on. The challenge now is not “what to measure ” but “how to analyze the data to extract meaningful data and turn those insights into applications”. While the development of NGS technologies and the generation of massive data has provided opportunities for a new field called “bioinformatics” to grow significantly, it has also...