ArnovanHilten/GenNet
Framework for Interpretable Neural Networks
This tool helps genetics researchers and computational biologists create and analyze neural networks for predicting phenotypes based on genetic data. You input genetic variant data (like from PLINK or VCF files) along with subject phenotypes and a custom network structure based on biological knowledge (like gene annotations). The output is an interpretable neural network that highlights which genetic connections are most important for the predicted trait.
115 stars. No commits in the last 6 months.
Use this if you need to build interpretable predictive models from genetic data, where you want to incorporate existing biological knowledge to define the network structure.
Not ideal if you are looking for a black-box machine learning solution or if your primary data is not genetics-related.
Stars
115
Forks
18
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 26, 2025
Commits (30d)
0
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