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

by Charles Ravarani, Natasha Latysheva

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.

434 pages, Paperback

Published August 26, 2025

Reviews

[Josua Naiborhu · Rating 5 out of 5]

The author really explains the concepts in a plain language. i love how each term laid out.

[Ayyuce Demirbas]

It's going to be hard for me to read because, sadly, the examples in the book are written in JAX, which isn’t a very popular ML library, instead of TensorFlow or PyTorch

[Medhat Ullah · Rating 4 out of 5]

The framework used was JAX, but even so, the book was absolutely fantastic