Deep Learning for Biology: Harness AI to Solve Real-World Biology Problems - Softcover

Ravarani, Charles; Latysheva, Natasha

Softcover

ISBN 10: 1098168038 ISBN 13: 9781098168032

Publisher: O'Reilly Media, 2025

Synopsis

Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems.

Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data.

• Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection
• Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders
• Use Python and interactive notebooks for hands-on learning
• Build problem-solving intuition that generalizes beyond biology

Whether you're exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.

About this title

About the Authors

Charles Ravarani is a biologist and software engineer who is currently Chief Technology Officer at biotx.ai, a computational drug discovery startup. He completed his PhD and post-doc in computational biology at the University of Cambridge, and in addition to his outstanding academic contributions, Charles is a software development veteran, has consulted various organizations, and has a passion for teaching programming and machine learning topics.

Natasha Latysheva is a biologist and machine learning practitioner who is currently a Senior Research Engineer at Google DeepMind, specializing in deep learning for genomics. With a PhD in computational biology from the University of Cambridge and experience across several machine learning domains, her expertise is in bridging the gap between biology and machine learning. She is passionate about machine learning education and making complex technical topics accessible and exciting.

From the Back Cover

Bridge the gap between biology and cutting-edge machine learning techniques with this essential guide. Train and interpret deep learning models and answer fundamental biological questions. Whether your background is in biology, machine learning, or software engineering, this book empowers you with the practical skills needed for your deep learning journey.

Through step-by-step recipes, authors Natasha Latysheva and Charles Ravarani teach you to train models for tasks like understanding gene regulatory logic, uncovering insights about protein families, and exploring subcellular localization. Each chapter provides self-contained mini-projects that serve as templates for approaching complex biological problems.

By the end of this book, you'll be able to:

• Approach biological problems with a deep learning perspective
• Train and interpret deep learning models to explore complex biological data
• Leverage Python code and iPython notebooks to extend your projects
• Transform your insights into actionable solutions in medicine, genomics, and biotechnology

Edition details

• Publisher: O'Reilly Media
• Publication date: 2025
• Language: English
• ISBN 10: 1098168038
• ISBN 13: 9781098168032
• Binding: Paperback
• Number of pages: 434