27 citations · 148 across the 22 of their papers we have counts for
23 papers
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…
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…
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Zehui Li, Xiangyu Zhao, Mingzhu Shen +3
Graphs are widely used to encapsulate a variety of data formats, but real-world networks often involve complex node relations beyond only being pairwise. While hypergraphs and hier…
Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations
Juan Kuntz, Philipp Thomas, Guy-Bart Stan +1
Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves solving a set of linear equations. In most cases of interest, the number of equations is in…