EpiAwareNet is a prior-guided multi-omic Transformer that uses gene-peak cross-attention for adaptive accessibility aggregation and bulk GRN priors for weak supervision to improve single-cell GRN reconstruction over baselines.
Advantages and limitations of current network inference methods
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Cell-to-cell variability selects for aligned, motif-enriched gene regulatory networks that are robust to developmental noise and mutations.
citing papers explorer
-
Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
EpiAwareNet is a prior-guided multi-omic Transformer that uses gene-peak cross-attention for adaptive accessibility aggregation and bulk GRN priors for weak supervision to improve single-cell GRN reconstruction over baselines.
-
How is gene-regulatory evolution affected by cell-to-cell variability?
Cell-to-cell variability selects for aligned, motif-enriched gene regulatory networks that are robust to developmental noise and mutations.