41 citations · 79 across the 12 of their papers we have counts for
12 papers
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
Zehao Dong, Muhan Zhang, Yixin Chen
Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation lea…
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Zehao Dong, Qihang Zhao, Philip R. O. Payne +6
Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Gr…
Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy
Zehao Dong, Yixin Chen, Hiram Gay +4
Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in aut…
DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks
Zehao Dong, Yixin Chen, Tianyu Zhao
Dose-Volume Histogram (DVH) prediction is fundamental in radiation therapy that facilitate treatment planning, dose evaluation, plan comparison and etc. It helps to increase the ab…
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
Chenfeng Zhao, Zehao Dong, Yixin Chen +2
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising exe…
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
Zehao Dong, Muhan Zhang, Philip R. O. Payne +5
The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a t…
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
Zehao Dong, Weidong Cao, Muhan Zhang +3
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs amo…
Time Associated Meta Learning for Clinical Prediction
Hao Liu, Muhan Zhang, Zehao Dong +5
Rich Electronic Health Records (EHR), have created opportunities to improve clinical processes using machine learning methods. Prediction of the same patient events at different ti…
Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
Zehao Dong, Heming Zhang, Yixin Chen +2
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination predictio…
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Zehao Dong, Muhan Zhang, Fuhai Li +1
Optimization of directed acyclic graph (DAG) structures has many applications, such as neural architecture search (NAS) and probabilistic graphical model learning. Encoding DAGs in…
TOI-3714 b and TOI-3629 b: Two gas giants transiting M dwarfs confirmed with HPF and NEID
Caleb I. Cañas, Shubham Kanodia, Chad F. Bender +36
We confirm the planetary nature of two gas giants discovered by TESS to transit M dwarfs. TOI-3714 () is an M2 dwarf hosting a hot Jupiter ($M_p=0.70 \pm 0.03~\ma…
Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare
Zehao Dong, Heming Zhang, Yixin Chen +1
We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination therapies in an inductive and interpretable manner. Though deep learning algorithm…