Learn concepts, methodologies, and applications of deep learning
for building predictive models from complex genomics data sets to
overcome challenges in the life sciences and biotechnology
industries Key Features Apply deep learning algorithms to solve
real-world problems in the field of genomics Extract biological
insights from deep learning models built from genomic datasets
Train, tune, evaluate, deploy, and monitor deep learning models for
enabling predictions in genomics Book DescriptionDeep 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. What you will learn Discover the
machine learning applications for genomics Explore deep learning
concepts and methodologies for genomics applications Understand
supervised deep learning algorithms for genomics applications Get
to grips with unsupervised deep learning with autoencoders Improve
deep learning models using generative models Operationalize deep
learning models from genomics datasets Visualize and interpret deep
learning models Understand deep learning challenges, pitfalls, and
best practices Who this book is forThis deep learning book is for
machine learning engineers, data scientists, and academicians
practicing in the field of genomics. It assumes that readers have
intermediate Python programming knowledge, basic knowledge of
Python libraries such as NumPy and Pandas to manipulate and parse
data, Matplotlib, and Seaborn for visualizing data, along with a
base in genomics and genomic analysis concepts.
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