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1. residualbind residualbind Public

   "Global Importance Analysis: A Method to Quantify Importance of Genomic Features in Deep Neural Networks" by Koo et al.

   Jupyter Notebook 11 3

2. tfomics tfomics Public

   Python 4 6

3. GOPHER GOPHER Public

   Forked from shtoneyan/gopher

   Toolset for training quantitative sequence to function models.

   Jupyter Notebook 2

4. evoaug evoaug Public

   Evolution-inspired data augmentations for PyTorch-based models for regulatory genomics

   Python 19 4

5. creme-nn creme-nn Public

   https://www.biorxiv.org/content/10.1101/2023.07.03.547592v2

   Python 27 3

6. squid-nn squid-nn Public

   Forked from evanseitz/squid-nn

   surrogate quantitative interpretability for deepnets

   Python 1