pinellolab/DNA-Diffusion
DNA-Diffusion applies diffusion probabilistic models to generate 200bp cell type-specific synthetic regulatory DNA sequences.
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DNA-Diffusion is a generative deep learning model that adapts the diffusion probabilistic framework (similar to Stable Diffusion) for the domain of genomics. It trains on genomic data to generate novel regulatory DNA sequences conditioned on cell type, enabling researchers to design synthetic regulatory elements. The model handles sequence generation as a denoising process and can be used for applications in synthetic biology and gene regulation research.
Frequently asked
- What is pinellolab/DNA-Diffusion?
- DNA-Diffusion applies diffusion probabilistic models to generate 200bp cell type-specific synthetic regulatory DNA sequences.
- Is DNA-Diffusion open source?
- Yes — pinellolab/DNA-Diffusion is an open-source project tracked on heatdrop.
- What language is DNA-Diffusion written in?
- pinellolab/DNA-Diffusion is primarily written in Python.
- How popular is DNA-Diffusion?
- pinellolab/DNA-Diffusion has 485 stars on GitHub.
- Where can I find DNA-Diffusion?
- pinellolab/DNA-Diffusion is on GitHub at https://github.com/pinellolab/DNA-Diffusion.