A survey on hypergraph neural networks: an in-depth and step-by-step guide
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda …
applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda …
Ehragent: Code empowers large language models for complex tabular reasoning on electronic health records
Large language models (LLMs) have demonstrated exceptional capabilities in planning and
tool utilization as autonomous agents, but few have been developed for medical problem …
tool utilization as autonomous agents, but few have been developed for medical problem …
Weakly-supervised scientific document classification via retrieval-augmented multi-stage training
Scientific document classification is a critical task for a wide range of applications, but the
cost of collecting human-labeled data can be prohibitive. We study scientific document …
cost of collecting human-labeled data can be prohibitive. We study scientific document …
Counterfactual learning on graphs: A survey
Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …
Learning task-aware effective brain connectivity for fmri analysis with graph neural networks
Functional magnetic resonance imaging (fMRI) has become one of the most common
imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have …
imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have …
R-mixup: Riemannian mixup for biological networks
Biological networks are commonly used in biomedical and healthcare domains to effectively
model the structure of complex biological systems with interactions linking biological entities …
model the structure of complex biological systems with interactions linking biological entities …
Ram-ehr: Retrieval augmentation meets clinical predictions on electronic health records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
Open visual knowledge extraction via relation-oriented multimodality model prompting
Images contain rich relational knowledge that can help machines understand the world.
Existing methods on visual knowledge extraction often rely on the pre-defined format (eg …
Existing methods on visual knowledge extraction often rely on the pre-defined format (eg …
Ptgb: Pre-train graph neural networks for brain network analysis
The human brain is the central hub of the neurobiological system, controlling behavior and
cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis …
cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis …
Beyond known reality: Exploiting counterfactual explanations for medical research
This study employs counterfactual explanations to explore" what if?" scenarios in medical
research, with the aim of expanding our understanding beyond existing boundaries …
research, with the aim of expanding our understanding beyond existing boundaries …