50 citations · 69 across the 12 of their papers we have counts for
12 papers
Graph Sampling-based Meta-Learning for Molecular Property Prediction
Xiang Zhuang, Qiang Zhang, Bin Wu +3
Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been…
Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection
Jiawei Liu, Jingyi Xie, Fanrui Zhang +2
The explosive growth of rumors with text and images on social media platforms has drawn great attention. Existing studies have made significant contributions to cross-modal informa…
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Lingbing Guo, Weiqing Wang, Zhuo Chen +5
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies. With the initial state of a system as input, the recent graph neural netwo…
Deep Reinforcement Learning for Entity Alignment
Lingbing Guo, Yuqiang Han, Qiang Zhang +1
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The…
On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification
Yunqi Miao, Nianchang Huang, Xiao Ma +2
Visible-infrared person re-identification (VI-ReID) has been challenging due to the existence of large discrepancies between visible and infrared modalities. Most pioneering approa…
Prompt-Guided Injection of Conformation to Pre-trained Protein Model
Qiang Zhang, Zeyuan Wang, Yuqiang Han +3
Pre-trained protein models (PTPMs) represent a protein with one fixed embedding and thus are not capable for diverse tasks. For example, protein structures can shift, namely protei…
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
Yin Fang, Zhuo Chen, Xiaohui Fan +1
Machine learning, notably deep learning, has significantly propelled molecular investigations within the biochemical sphere. Traditionally, modeling for such research has centered…
OntoProtein: Protein Pretraining With Gene Ontology Embedding
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang +6
Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language…
Molecular Contrastive Learning with Chemical Element Knowledge Graph
Yin Fang, Qiang Zhang, Haihong Yang +7
Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive…
Principled Representation Learning for Entity Alignment
Lingbing Guo, Zequn Sun, Mingyang Chen +3
Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of…
Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding
Ying Shen, Qiang Zhang, Jin Zhang +3
Automatic text classification (TC) research can be used for real-world problems such as the classification of in-patient discharge summaries and medical text reports, which is bene…
MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs
Kai Lei, Kaiqi Yuan, Qiang Zhang +1
We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitut…