🧬 Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨
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Updated
Oct 10, 2024 - Python
🧬 Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨
A set of tutorials for how to use all the tools in ML4GLand
Genomic sequence preprocessing toolkit
surrogate quantitative interpretability for deepnets
Data-driven design of context-specific regulatory elements
Elucidating the Utility of Genomic Elements with Neural Nets
squid repository for manuscript analysis
Interpreting sequence-to-function machine learning models
Repository documenting applications of the ML4GLand suite on published datasets
Datasets for benchmarking, testing and developing in EUGENe
Deep learning model for non-coding regulatory variants
Motif representation and analysis toolkit in Python
A curated list of regulatory genomics papers and resources.
Deep Unfolded Convolutional Dictionary Learning for motif discovery.
Threshold and p-value computations for Position Weight Matrices
A Hugo-based deployment of biocomputeobject.org
Analyze the active regulatory region of DNA using FFNN and CNN
Prediction of transcription factor binding based on DNA sequence
lsgkm+gkmexplain with regression functionality. Builds off kundajelab/lsgkm (which has gkmexplain), which in turn builds off Dongwon-Lee/lsgkm (the original lsgkm repo)
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