Search Results for author:
Found 10 papers, 3 papers with code
Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy
A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph.
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
However, we found two major limitations of existing GNNs in omics data analysis, i. e., limited-prediction (diagnosis) accuracy and limited-reproducible biomarker identification capacity across multiple datasets.
DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks
Dose-Volume Histogram (DVH) prediction is fundamental in radiation therapy that facilitate treatment planning, dose evaluation, plan comparison and etc.
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
In this paper, we propose GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment, and FPGA implementations of 6 well-tuned GNN HLS kernels.
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
We theoretically reveal the trade-off of expressivity and stability in graph-canonization-enhanced GNNs.
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
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 among circuit specifications.
Time Associated Meta Learning for Clinical Prediction
We view time-associated disease prediction as classification tasks at multiple time points.
Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions.
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
In this work, we propose a Parallelizable Attention-based Computation structure Encoder (PACE) that processes nodes simultaneously and encodes DAGs in parallel.
Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare
Though deep learning algorithms are widely used in the drug synergy prediction problem, it is still an open problem to formulate the prediction model with biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI collaboration in healthcare systems.
