Deep Learning for Biological Systems
Building Computational Models with PyTorch for Real-World Biological Problems
You'll move from understanding PyTorch fundamentals to building production-ready deep learning models that solve real biological challenges. This book guides you through the complete workflow of computational biology projects: from formulating biological questions as machine learning problems, through data acquisition and preprocessing, to model development, validation, and deployment. You'll learn how to work with diverse biological data types—genomic sequences, protein structures, medical images, and time-series measurements—using PyTorch's flexible architecture. Each chapter combines theoretical foundations with practical code examples, showing you how to handle the messy realities of biological data including class imbalance, missing values, and limited samples. You'll discover how to leverage transfer learning, implement custom loss functions for biological objectives, and validate models using domain-specific metrics. By the end, you'll have the skills to design computational solutions that bridge the gap between cutting-edge machine learning and meaningful biological discovery.
Est. Length
222 pages (11 chapters)
Language
English
Publication date
Jun 19, 2026
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