@inproceedings{
li2023latent,
title={Latent Diffusion Model for {DNA} Sequence Generation},
author={Zehui Li and Yuhao Ni and Tim Huygelen and Akashaditya Das and Guoxuan Xia and Guy-Bart Stan and Yiren Zhao},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=IxyMZngWen}
}
The harnessing of machine learning, especially deep generative models, has opened up promising avenues in the field of synthetic DNA sequence generation. Whilst Generative Adversarial Networks (GANs) have gained traction for this application, they often face issues such as limited sample diversity and mode collapse. On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation. In light of this, we propose a novel *latent diffusion* model, DiscDiff, tailored for discrete DNA sequence generation. By simply embedding discrete DNA sequences into a continuous latent space using an autoencoder, we are able to leverage the powerful generative abilities of continuous diffusion models for the generation of discrete data. Additionally, we introduce Fréchet Reconstruction Distance (FReD) as a new metric to measure the sample quality of DNA sequence generations. Our DiscDiff model demonstrates an ability to generate synthetic DNA sequences that align closely with real DNA in terms of Motif Distribution, Latent Embedding Distribution (FReD), and Chromatin Profiles. Additionally, we contribute a comprehensive cross-species dataset of 150K unique promoter-gene sequences from 15 species, enriching resources for future generative modelling in genomics. We have made our code and data publicly available at https://github.com/Zehui127/Latent-DNA-Diffusion.
Latent Diffusion Model for DNA Sequence Generation
Zehui Li Yuhao Ni Tim August B. Huygelen Akashaditya Das Guoxuan Xia Guy-Bart Stan Yiren Zhao