6 citations · 12 across the 4 of their papers we have counts for
4 papers
Absorb & Escape: Overcoming Single Model Limitations in Generating Genomic Sequences
Zehui Li, Yuhao Ni, Guoxuan Xia +4
Abstract Recent advances in immunology and synthetic biology have accelerated the development of deep generative methods for DNA sequence design. Two dominant approaches in this fi…
DiscDiff: Latent Diffusion Model for DNA Sequence Generation
Zehui Li, Yuhao Ni, William A V Beardall +4
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA se…
Latent Diffusion Model for DNA Sequence Generation
Zehui Li, Yuhao Ni, Tim August B. Huygelen +4
The harnessing of machine learning, especially deep generative models, has opened up promising avenues in the field of synthetic DNA sequence generation. Whilst Generative Adversar…
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer
Zehui Li, Akashaditya Das, William A V Beardall +2
Given the increasing volume and quality of genomics data, extracting new insights requires interpretable machine-learning models. This work presents Genomic Interpreter: a novel ar…