Evidence map›Paper›PMID 42039608›Full record

ArticlebioRxiv : the preprint server for biology2026

A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.

Xiaohui Jiang, Jichun Xie

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

2 authors.

Xiaohui JiangDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Jichun XieDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

Funding

The Duke Senescent Cell Evaluations in Normal Tissues (SCENT) Mapping CenterU54AG075936 · NIA · DUKE UNIVERSITY · PI NIXON, ANDREW B. · 2021 to 2025
$12.7M
Multiscale Modeling of Influenza Neutralizing Antibody and Fc Effector BiologyU01AI186999 · NIAID · DUKE UNIVERSITY · PI Cliburn C Chan, Roger Keith Reeves · 2025 to 2026
$2.1M
New computational methods to dynamically pinpointing the subregions carrying disease-associated rare variantsR01HG012555 · NHGRI · DUKE UNIVERSITY · PI XIE, JICHUN · 2022 to 2025
$1.6M
NHGRI NIH HHS R01 HG012555NIAID NIH HHS U01 AI186999NIA NIH HHS U54 AG075936
6 · The paper itself

Abstract

Current single-cell foundation models rely on language-model architectures that ignore transcriptomic data distributions, often underperforming specialized methods. We introduce xVERSE, a transcriptomics-native foundation model coupling batch-invariant representation learning with the probabilistic generation of expression profiles. xVERSE outperforms the leading foundation and batch-effect correction methods in representation learning by

Indexed as

generative pre-trainingsingle-cell omicsspatial omicstranscriptomics-native foundation modelvirtual cell synthesis

Identifiers

PMID42039608
PMCPMC13104837

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.