10 citations · 23 across the 6 of their papers we have counts for
7 papers
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
Jiahua Rao, Shuangjia Zheng, Sijie Mai +1
Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene intera…
Learning Attributed Graph Representations with Communicative Message Passing Transformer
Jianwen Chen, Shuangjia Zheng, Ying Song +2
Constructing appropriate representations of molecules lies at the core of numerous tasks such as material science, chemistry and drug designs. Recent researches abstract molecules…
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning
Shuangjia Zheng, Sijie Mai, Ya Sun +2
Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entit…
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
Jiahua Rao, Shuangjia Zheng, Yuedong Yang
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and mol…
BioNavi-NP: Biosynthesis Navigator for Natural Products
Shuangjia Zheng, Tao Zeng, Chengtao Li +4
Nature, a synthetic master, creates more than 300,000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs. To date,…
Communicative Message Passing for Inductive Relation Reasoning
Sijie Mai, Shuangjia Zheng, Yuedong Yang +1
Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are…