From Statistical Physics to Data-Driven Modelling: with Applications to Quantitative Biology
Bridge the gap between statistical physics and modern data-driven methods with the comprehensive and interdisciplinary volume “From Statistical Physics to Data-Driven Modelling: With Applications to Quantitative Biology.” This book offers a unique and cutting-edge perspective on how the principles of physics can be seamlessly integrated with data science tools to model complex biological systems.
Ideal for graduate students, researchers, and professionals working in physics, systems biology, and computational biology, this title presents both the theoretical foundations and practical approaches needed to tackle real-world biological questions using statistical modeling and machine learning techniques.
Key Features:
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Provides a step-by-step introduction to statistical physics concepts relevant to biological systems
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Emphasizes data-driven modeling, including Bayesian inference, stochastic processes, and Monte Carlo methods
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Includes real-life case studies in quantitative biology: gene regulation, population dynamics, neural networks, protein interactions, and more
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Discusses the interplay between theory and data, helping readers learn how to extract insights from experimental and observational datasets
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Explains advanced topics like entropy, inference algorithms, Markov models, and information theory
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Offers hands-on examples with reproducible computational approaches suitable for Python and R users
This book is more than just theory—it’s a practical guide for integrating physics-based thinking with the latest in computational biology and data analytics. Whether you’re building models of cellular behavior or interpreting noisy biological signals, this title gives you the tools and frameworks to succeed.
