Deep Learning for Biology

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 detectionApply architectures like convolutional neural networks, transformers, graph neural networks, and autoencodersUse Python and interactive notebooks for hands-on learningBuild problem-solving intuition that generalizes beyond biologyWhether youare 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.

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.