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, and build problem-solving intuition that generalizes beyond biology.
Whether you are 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.
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