6 citations · 7 across the 9 of their papers we have counts for
9 papers
SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning
Zhaohan Meng, Zaiqiao Meng, Siwei Liu +3
Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and phenotypes. Existing agents typ…
A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?
Zhaohan Meng, Zhen Bai, Ke Yuan +4
Protein-ligand modeling underpins computational drug discovery and molecular design. Existing protein-ligand benchmarks typically evaluate whether a protein and ligand interact and…
Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies
Farzaneh Seyedshahi, Francesca Damiola, Sylvie Lantuejoul +2
Accurate subtype classification and outcome prediction in mesothelioma are essential for guiding therapy and patient care. Most computational pathology models are trained on large…
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
Zhaohan Meng, Zaiqiao Meng, Siwei Liu +3
Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across multiple turns of conversation. Existing mu…
Divide and Conquer Self-Supervised Learning for High-Content Imaging
Lucas Farndale, Paul Henderson, Edward W Roberts +1
Self-supervised representation learning methods often fail to learn subtle or complex features, which can be dominated by simpler patterns which are much easier to learn. This limi…
FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
Zhaohan Meng, Zaiqiao Meng, Ke Yuan +1
Predicting drug-target interaction (DTI) is critical in the drug discovery process. Despite remarkable advances in recent DTI models through the integration of representations from…
Training-Free Pretrained Model Merging
Zhengqi Xu, Ke Yuan, Huiqiong Wang +3
Recently, model merging techniques have surfaced as a solution to combine multiple single-talent models into a single multi-talent model. However, previous endeavors in this field…
Synthetic Privileged Information Enhances Medical Image Representation Learning
Lucas Farndale, Chris Walsh, Robert Insall +1
Multimodal self-supervised representation learning has consistently proven to be a highly effective method in medical image analysis, offering strong task performance and producing…
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology
Lucas Farndale, Robert Insall, Ke Yuan
Computational pathology models rarely utilise data that will not be available for inference. This means most models cannot learn from highly informative data such as additional imm…