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Updated: Apr 11, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Published on: March 1, 2024

GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model.

Mingxuan Zhang¹, Vinay Swamy¹, Rowan Cassius¹

¹Columbia University, New York, NY, USA.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

Summary

This summary is machine-generated.

We developed GREmLN, a novel foundation model for single-cell RNA data. This gene regulatory network-embedding model captures complex gene dependencies, improving cell type annotation and biological predictions.

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Area of Science:

Computational Biology
Genomics
Machine Learning

Background:

Large-scale single-cell data requires advanced models.
Standard transformers struggle with orderless gene features in transcriptomics.
Molecular interaction graphs offer structural information for gene dependencies.

Purpose of the Study:

To introduce GREmLN, a foundation model for single-cell transcriptomics.
To leverage graph signal processing for biologically informed gene embeddings.
To improve the analysis of complex regulatory relationships in single-cell data.

Main Methods:

Developed GREmLN (Gene Regulatory Embedding-based Large Neural model).
Integrated graph signal processing within the attention mechanism.
Utilized gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks.

Main Results:

GREmLN captures transcriptomics landscapes effectively.
Achieved superior performance in cell type annotation and graph structure understanding.
Demonstrated high accuracy in fine-tuned reverse perturbation prediction tasks.

Conclusions:

GREmLN provides a unified, interpretable framework for single-cell data.
The model captures complex, long-range regulatory dependencies.
Graph-structured inductive biases lead to parameter efficiency and faster training.