75 citations · 286 across the 37 of their papers we have counts for
4 papers · 1 filter
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Yulun Wu, Robert A. Barton, Zichen Wang +5
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variatio…
Variational Causal Inference
Yulun Wu, Layne C. Price, Zichen Wang +3
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the out…
Efficient and effective training of language and graph neural network models
Vassilis N. Ioannidis, Xiang Song, Da Zheng +6
Can we combine heterogenous graph structure with text to learn high-quality semantic and behavioural representations? Graph neural networks (GNN)s encode numerical node attributes…
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features
Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis +2
Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data.…