yhsure/riemannian-generative-decoder
Preprint | Previously at GenBio ICML 2025
This project helps scientists and researchers understand complex biological processes by creating simplified visual representations of intricate data. It takes raw biological measurements, like genetic sequences or cell states, and outputs clear, interpretable visualizations that reveal underlying patterns and relationships, even when the data doesn't fit neatly into traditional charts. Scientists in genomics, cell biology, and evolutionary studies can use this to make sense of their experimental results.
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Use this if you need to visualize and interpret complex biological data where standard linear relationships don't apply, such as tracking human migration patterns from DNA or understanding cell cycle progression.
Not ideal if your data is simple and linearly structured, or if you need a quick, off-the-shelf tool for basic statistical analysis without deep representation learning.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 20, 2025
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