Deep Learning for Biology

Preface

This book introduces you to the fascinating intersection of biology and deep learning. It’s written for both biologists eager to acquire computational skills and computational practitioners curious about applying their expertise to biological problems. This fusion of disciplines is already transforming biotechnology and medicine—and is poised to become foundational across the life sciences.

The material in this book is pitched to be introductory, guiding you from the basics to more intermediate concepts. We aim to balance practical code examples with clear explanations, making new terms and ideas accessible. Real-world Python code appears early and often, helping you develop hands-on intuition. While deep learning is a powerful tool, it’s not a one-size-fits-all solution—we emphasize the importance of understanding your data and framing your problem thoughtfully before diving into modeling. We encourage you to start simple, build modular and debuggable code, and add complexity only when it serves a clear purpose.

Although this book is designed for beginners, each chapter builds on the last to develop a practical, end-to-end workflow for applying machine learning to biological data. Our goal is to equip you with tools that are robust enough to solve real-world problems and flexible enough to adapt to your own research questions. In the final chapter, for example, we reproduce key results from a recent Nature Methods paper that uses deep learning to uncover spatial protein ...