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EON Ensemble

EON (Ecological Oncology Networks) maps histopathology whole-slide images to per-patch cell-state program (CP) predictions by training a neighborhood-aware model (EONv) on scRNA-seq-derived meta-programs. Those predictions are then used to build spatial patch graphs and detect recurrent tissue motifs across cancer types. The core model, EONv, combines a vision encoder with a cross-attention neighborhood aggregation head that is trained end-to-end with a MSE objective against NMF-derived CP soft labels.

Usage

from EON.models import EONEnsemble
model = EONEnsemble.from_hub("ViggyVenkat/EON")

Outputs are z-scores per CP relative to the training cohort mean. Use model.target_means and model.target_stds to invert to original NMF-usage scale.

Package

https://github.com/Viggyvenkat/EON

Contact

EON was developed in the De Laboratory at the Rutgers Cancer Institute.
Contact: Vignesh V. Venkat; vvv11@scarletmail.rutgers.edu

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