6 citations · 13 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…