Abstract
Background
Mutual Hazard Networks (MHNs) are statistical models for analyzing (genetic) cancer progression. Many cancers develop silently and are only noticeable when they have significantly progressed, creating an observational gap until diagnosis. MHNs bridge this gap by reconstructing the underlying dynamics of disease progression.
Summary
We present mhn, a Python package for dynamic cancer ...
Abstract
Background
Mutual Hazard Networks (MHNs) are statistical models for analyzing (genetic) cancer progression. Many cancers develop silently and are only noticeable when they have significantly progressed, creating an observational gap until diagnosis. MHNs bridge this gap by reconstructing the underlying dynamics of disease progression.
Summary
We present mhn, a Python package for dynamic cancer progression analysis using MHNs. It trains an MHN model from tumor genotypes. mhn overcomes challenges of numerical efficiency in model training by making use of state space restriction, allowing training MHNs with more than 100 mutational events, 5 times more than was possible before. The package offers (a) reconstruction of the most likely evolutionary history of tumors, (b) sampling of artificial tumor histories, and (c) visualization of genomic interactions and likely progression trajectories. These features substantially extend earlier implementations, providing a fast and user-friendly framework for researchers and clinicians to study cancer dynamics.
Availability and Documentation
mhn can be installed from PyPI using pip and is available under the MIT License on GitHub (https://github.com/spang-lab/LearnMHN). Installation instructions and package functionalities are detailed on GitHub and PyPI, with a comprehensive guide on Read the Docs (https://learnmhn.readthedocs.io/en/latest/index.html) and a Jupyter notebook on GitHub to help users explore the package.
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