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

Introduction to Biopython for genomic data analysis

In this section, you will be familiarized with the basics of Biopython, and in the subsequent section, you will use Biopython for solving a real-world research question in genomics.

What is Biopython?

Biopython is a popular Python package developed by Chapman and Chang, mainly intended for biological researchers and data miners to analyze genomic data. It was written mainly in Python but also has support for C code to optimize complex computations. It can be run on any operating system (Windows, Linux, and macOS). Biopython provides lots of functionalities to support genomic data and it makes it easy to use Python for genomic data analysis through reusable modules and classes. In addition to providing basic and advanced genomic functionalities, it also has support for parsers for various popular bioinformatics file formats such as BLAST, ClustalW, FASTA, and GenBank, as well as support online databases and servers such as NCBI...