4 citations · 11 across the 12 of their papers we have counts for
13 papers
When Clipping Reverses Correction: Failure Dynamics of Pointwise Forward-KL On-Policy Self-Distillation
Di Huang, Hao Li, Yixin Chen +1
On-policy self-distillation (OPSD) trains a student on its own generated responses using feedback from the same model conditioned on privileged information. On mathematical reasoni…
HistoMet: A Pan-Cancer Deep Learning Framework for Prognostic Prediction of Metastatic Progression and Site Tropism from Primary Tumor Histopathology
Yixin Chen, Ziyu Su, Lingbin Meng +4
Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it will disseminate directly from hi…
GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
Heming Zhang, Di Huang, Wenyu Li +4
In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways a…
OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling
Heming Zhang, Tim Xu, Dekang Cao +22
With the rapid growth of large-scale single-cell omic datasets, omic foundation models (FMs) have emerged as powerful tools for advancing research in life sciences and precision me…
KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognition
Heming Zhang, Wenyu Li, Di Huang +4
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that plays a crucial role in information extraction, question answering, and knowledge-bas…
Large Language Models Meet Graph Neural Networks for Text-Numeric Graph Reasoning
Haoran Song, Jiarui Feng, Guangfu Li +4
In real-world scientific discovery, human beings always make use of the accumulated prior knowledge with imagination pick select one or a few most promising hypotheses from large a…
GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
Heming Zhang, Di Huang, Yixin Chen +1
The integration of multi-omic data is pivotal for understanding complex diseases, but its high dimensionality and noise present significant challenges. Graph Neural Networks (GNNs)…
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…