6 citations · 13 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning
Zehui Li, Vallijah Subasri, Guy-Bart Stan +2
Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic diseases. The rapid decrease in…
Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation
Xiangyu Zhao, Zehui Li, Mingzhu Shen +3
Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation m…
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…