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Methods for inferring low-dimensional representations of high-dimensional phenotype spaces.
Jupyter Notebook Python
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.gitignore
LICENSE.md
README.md
dimreducer.py
general_autoencoder.py
laplacian_variational_autoencoder.py
mortality_weighted_variational_age_autoencoder.py
multiphenotype_utils.py
setup.py
sparse_correlation_variational_age_autoencoder.py
sparse_variational_age_autoencoder.py
standard_autoencoder.py
toy_data_example.ipynb
variational_age_autoencoder.py
variational_autoencoder.py
variational_longitudinal_monotonic_rate_of_aging_autoencoder.py
variational_rate_of_aging_autoencoder.py
variational_rate_of_aging_monotonic_autoencoder.py

README.md

This code contains methods for inferring low-dimensional representations of high-dimensional phenotype spaces.

To see an example, please look at toy_data_example.ipynb.

We are happy to make this code available for any commercial or non-commercial actor to use. This code is available under the MIT License (see LICENSE.md).

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