70 citations · 197 across the 15 of their papers we have counts for
6 papers · 1 filter
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning
Chenqing Hua
Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowl…
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Chenqing Hua, Yong Liu, Dinghuai Zhang +6
Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or prote…
ReactZyme: A Benchmark for Enzyme-Reaction Prediction
Chenqing Hua, Bozitao Zhong, Sitao Luan +4
Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations. Predicting enzyme functions is essen…
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sitao Luan, Qincheng Lu, Chenqing Hua +3
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily c…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua, Connor Coley, Guy Wolf +2
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of mul…