3 citations · 5 across the 5 of their papers we have counts for
6 papers · 1 filter
Multi-Scale Protein Structure Modelling with Geometric Graph U-Nets
Chang Liu, Vivian Li, Linus Leong +3
Geometric Graph Neural Networks (GNNs) and Transformers have become state-of-the-art for learning from 3D protein structures. However, their reliance on message passing prevents th…
HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search
Yusi Fan, Tian Wang, Zhiying Yan +8
Feature selection is a combinatorial optimization problem that is NP-hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature converg…
MolProphecy: Bridging Medicinal Chemists' Knowledge and Molecular Pre-Trained Models via a Multi-Modal Framework
Jianping Zhao, Qiong Zhou, Tian Wang +8
MolProphecy is a human-in-the-loop (HITL) multi-modal framework designed to integrate chemists' domain knowledge into molecular property prediction models. While molecular pre-trai…
DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data
Ruochi Zhang, Qian Yang, Xiaoyang Wang +10
The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional…
From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR
Ran Xu, Yiwen Lu, Chang Liu +5
Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHR…
PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding
Ruochi Zhang, Haoran Wu, Chang Liu +8
Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models t…