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.

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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.

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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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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.

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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.

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

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