6 citations · 13 across the 8 of their papers we have counts for
9 papers
KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy
Xueting Fang, Zehui Li, Yang Yang +5
Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions. In clinical practice, however, patients present information in different ways…
LDP: Parameter-Efficient Fine-Tuning of Multimodal LLM for Medical Report Generation
Tianyu Zhou, Junyi Tang, Zehui Li +2
Colonoscopic polyp diagnosis is pivotal for early colorectal cancer detection, yet traditional automated reporting suffers from inconsistencies and hallucinations due to the scarci…
Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning
Zehui Li, Vallijah Subasri, Yifei Shen +4
Large Language Models (LLMs) demonstrate remarkable generalizability across diverse tasks, yet genomic foundation models (GFMs) still require separate finetuning for each downstrea…
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…
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…