Evidence map›Paper›PMID 39975086›Full record

ArticlebioRxiv : the preprint server for biology2025

EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment.

Juncheng Wu, Changxin Wan, Zhicheng Ji, Yuyin Zhou, Wenpin Hou

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

5 authors.

Juncheng WuDepartment of Computer Science and Engineering, UC Santa Cruz.
Changxin WanDepartment of Biostatistics and Bioinformatics, Duke University.
Zhicheng JiDepartment of Biostatistics and Bioinformatics, Duke University.
Yuyin ZhouDepartment of Computer Science and Engineering, UC Santa Cruz.
Wenpin HouDepartment of Biostatistics, Mailman School of Public Health, Columbia University.

Funding

Methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics dataR35GM150887 · NIGMS · DUKE UNIVERSITY · PI Wenpin Hou · 2023 to 2026
$1.6M
Computational methods for in situ spatial transcriptomicsR35GM154865 · NIGMS · DUKE UNIVERSITY · PI Zhicheng Ji · 2024 to 2026
$1.2M
NIGMS NIH HHS R35 GM150887NIGMS NIH HHS R35 GM154865
6 · The paper itself

Abstract

Foundation models exhibit strong capabilities for downstream tasks by learning generalized representations through self-supervised pre-training on large datasets. While several foundation models have been developed for single-cell RNA-seq (scRNA-seq) data, there is still a lack of models specifically tailored for single-cell ATAC-seq (scATAC-seq), which measures epigenetic information in individual cells. The principal challenge in developing such a model lies in the vast number of scATAC peaks and the significant sparsity of the data, which complicates the formulation of peak-to-peak correlations. To address this challenge, we introduce

Identifiers

PMID39975086
PMCPMC11839112

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.