Search Results for author:
Found 12 papers, 6 papers with code
OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Joint LLM and GNN Modeling
These systems evolve under influences such as age, sex, diet, environmental exposures, and diseases, making them challenging to decode given the involvement of tens of thousands of genes and proteins.
KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognition
In this work, we introduce Knowledge Graph distilled for Named Entity Recognition (KoGNER), a novel approach that integrates Knowledge Graph (KG) distillation into NER models to enhance entity recognition performance.
GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
The integration of multi-omic data is pivotal for understanding complex diseases, but its high dimensionality and noise present significant challenges.
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.
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.
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k, t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem.
Ranked #3 on
Graph Regression
on ZINC-500k
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.
How Powerful are K-hop Message Passing Graph Neural Networks
Recently, researchers extended 1-hop message passing to K-hop message passing by aggregating information from K-hop neighbors of nodes simultaneously.
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.
Repurposing drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2
Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources.
Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models
Drug resistance is still a major challenge in cancer therapy.
