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Deep Learning for Biology: Harness AI to Solve Real-World Biology Problems
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.
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
2 people are currently reading
28 people want to read
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Displaying 1 - 4 of 4 reviews
August 25, 2025
The author really explains the concepts in a plain language. i love how each term laid out.
January 31, 2026
I absolutely loved reading this book. It's challenging, opened my eyes to many problems in Biology and how I can use Deep Learning for those challenges. It has a ton of theory and code, which is a great balance between giving the building blocks of (biology and ML) knowledge and very practical projects.
I highly recommend it for people who are interested in the intersection of ML and biology/medicine. It always covers 3 important parts: A biology primer, an ML primer, and the data problem we will be solving in each chapter.
These are topics in biology covered:
- Proteins
- DNA
- Drug-Drug Interaction
- Skin Cancer
- Spatial Organization in Cells
The ML topics covered:
- Transformers and Multi-Head Attention
- Latent Representation/Embeddings
- Large Language Models
- Convolutional Neural Networks
- Graph Neural Networks
- Autoencoders, Variational Autoencoders (VAEs), and Vector-Quantized Variational Autoencoders (VQ-VAEs)
It ends with a very insightful chapter about tips and tricks for Deep Learning in Biology
- Debugging strategies
- Common data issues
- Biology-specific issues
- Common model issues
- Handling poor performance
The notebooks are easy to follow, especially if you run them yourself and get a sense of the data and the ML algorithms. If you have experience with any ML framework, you'll be fine, even though it's all written in Flax.
I highly recommend it for people who are interested in the intersection of ML and biology/medicine. It always covers 3 important parts: A biology primer, an ML primer, and the data problem we will be solving in each chapter.
These are topics in biology covered:
- Proteins
- DNA
- Drug-Drug Interaction
- Skin Cancer
- Spatial Organization in Cells
The ML topics covered:
- Transformers and Multi-Head Attention
- Latent Representation/Embeddings
- Large Language Models
- Convolutional Neural Networks
- Graph Neural Networks
- Autoencoders, Variational Autoencoders (VAEs), and Vector-Quantized Variational Autoencoders (VQ-VAEs)
It ends with a very insightful chapter about tips and tricks for Deep Learning in Biology
- Debugging strategies
- Common data issues
- Biology-specific issues
- Common model issues
- Handling poor performance
The notebooks are easy to follow, especially if you run them yourself and get a sense of the data and the ML algorithms. If you have experience with any ML framework, you'll be fine, even though it's all written in Flax.
Want to read
October 16, 2025It'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
October 28, 2025
The framework used was JAX, but even so, the book was absolutely fantastic
Displaying 1 - 4 of 4 reviews