Evidence map›Paper›PMID 41959137›Full record

ArticlebioRxiv : the preprint server for biology2026

GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model.

Mingxuan Zhang, Vinay Swamy, Rowan Cassius, Léo Dupire, Charilaos Kanatsoulis, Evan Paull, Mohammed AlQuraishi, Theofanis Karaletsos, Andrea Califano

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mingxuan ZhangColumbia University, New York, NY, USA.ORCID 0009-0005-5013-5549
Vinay SwamyColumbia University, New York, NY, USA.
Rowan CassiusColumbia University, New York, NY, USA.
Léo DupireColumbia University, New York, NY, USA.
Charilaos KanatsoulisStanford University, Palo Alto, CA, USA.
Evan PaullChan Zuckerberg Biohub New York, New York, NY, USA.
Mohammed AlQuraishiColumbia University, New York, NY, USA.
Theofanis KaraletsosChan Zuckerberg Initiative, Redwood City, CA, USA.
Andrea CalifanoColumbia University, New York, NY, USA.ORCID 0000-0003-4742-3679

Funding

Predicting Cancer Cell Response to Endogenous and Exogenous Perturbations at the Single Cell LevelR35CA197745 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI ANDREA CALIFANO · 2015 to 2026
$11.5M
NCI NIH HHS R35 CA197745
6 · The paper itself

Abstract

The ever-increasing availability of large-scale single-cell profiles presents an opportunity to develop foundation models to capture cell properties and behavior. However, standard language models such as transformers benefits from sequentially structured data with well defined absolute or relative positional relationships, while single cell RNA data have orderless gene features. Molecular-interaction graphs, such as gene regulatory networks (GRN) or protein-protein interaction (PPI) networks, offer graph structure-based models that effectively encode both non-local gene token dependencies, as well as potential causal relationships. We introduce GREmLN (

Identifiers

PMID41959137
PMCPMC13060794

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.