Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology

Upendra Kumar Devisetty

€18.99 per month

4 (8 Ratings)

Paperback Nov 2022 270 pages 1st Edition

Upendra Kumar Devisetty

€18.99 per month

4 (8 Ratings)

Paperback Nov 2022 270 pages 1st Edition

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Key benefits

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

Description

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.

Who is this book for?

This 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.

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

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H2N Jul 14, 2023

This textbook offers a practical and informative overview of the convergence of machine learning and biotechnology. It caters to individuals with experience in both fields and serves as a comprehensive concept refresher. Particularly valuable is its introduction to deep learning models in the context of biology and their interpretability. The inclusion of projects and code snippets empowers readers to embark on future endeavors confidently.

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Emerald Mar 12, 2023

This is the only book I can find on this topic. It’s very useful for me as a person who is relatively familiar to classical machine learning but just step into the field of genomics. I notice some comments mentioning that the book is a bit shallow. However, the book is just right to me. I like the Genomics Data Analysis part, which gives concrete examples. By these examples, I get a taste and have an idea about what people in this field are doing. My favorite part of the book is that the author gives many use cases and code examples. I’m sure this is a big topic and agree that if you want to understand the topic deeper, having this book only is not enough. But I’ll strongly recommend this book as a start for people like me. Btw, giving the paper book buyers an additional digital copy is another thing I like about the book.

Sheena Apr 06, 2023

This book assumes technical knowledge so it is not for someone who is totally new to coding and deep learning. I think this book is a great primer for software developers who are new to genomics but have some solid fundamentals of classical machine learning and biology. It covers all the important topics of deep learning in genomics but you should not expect yourself to become an expert after reading this book. It saves you time, as a software developer who is new to genomics and deep learning, to sieve through the relevant topics and materials yourself by googling online. I wish I had this book handy when I did my onboarding for my previous job at a biotech startup. If you are looking for a primer to this topic, then this book is for you.

Ekta Jun 27, 2023

This book teaches you about deep learning approaches to solve problems in genomics, interpret biological insights from genomic datasets, and finally, operationalize deep learning models using open source tools to enable predictions for end users.Part 1: It introduces the fundamentals of genomic data analysis and machine learning for genomic data and its applications.Part 2: This covers the basic concepts of deep learning and how to transform raw genomics data into biological insights. It includes DNN, CNN, RNN, Auto-encoders and GAN models.Part 3: Final part covers the deep learning models using open source tools to enable predictions for end users. It includes building and tuning deep learning methods, model interpretability, deployment and monitoring. This part also covers challenges, and best practices for deep learning in Genomics.

YS Jan 30, 2023

OVERVIEWThis textbook is a great high level overview of the concepts, tools, and techniques at the intersection of ML and biotechnology, but it could benefit from a few more rounds of content balancing and visual inclusion.+ This textbook is massively practical for those who are looking to understand the intersection of ML and genetics, and already have some experience in both fields.+ I studied data science and genetics, and I found the textbook to be both a **thorough concept refresher** and a **useful introduction for DL bio models and their interpretability**.+ The **practical inclusion of projects and code snippets** made any future projects I pursue to be a lot more accessible now that I know what tools to begin with.+ Talking about **typical practices and use-cases of different models/packages and how to manage and optimize them** was a refreshing take, compared to many textbooks that lack little real-world application+ Chapters are generally well-organized and build upon knowledge from prior chapters. Summaries at end of chapters are concise but still provide a thorough overview of the chapter- The **balance of content allocated** between introductory vs more complex topics **is uneven**. This makes the later DL chapters less accessible to those without a thorough ML background, as some topics seem to be glossed over- The textbook could also **benefit from having more visuals** and **fewer blocks of text**, both of which would increase the digestibility of the work. There are some great figures (e.g. figure 5.3) but more to break up the text and visually explain tough concepts would be helpful.A lot of work has gone into the textbook and it is very useful, but it requires some visual reshaping and rebalancing of depth to improve the reader experience.

Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology

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