OgBench is the first benchmark platform for GNN graph-level prediction in the n << p omics regime and finds that common GNNs often underperform MLPs and classical baselines.
How attentive are graph attention networks? In International Conference on Learning Representations
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3roles
method 1polarities
use method 1representative citing papers
GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
Topology-aware large graph representations of polymer chains combined with masked pretraining on unlabeled data reduce prediction error for glass transition temperature by 5.1% compared to repeat-unit baselines.
citing papers explorer
-
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
OgBench is the first benchmark platform for GNN graph-level prediction in the n << p omics regime and finds that common GNNs often underperform MLPs and classical baselines.
-
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
-
It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
Topology-aware large graph representations of polymer chains combined with masked pretraining on unlabeled data reduce prediction error for glass transition temperature by 5.1% compared to repeat-unit baselines.