Evidence map›Paper›PMID 41674716›Full record

ArticleQuantitative biology (Beijing, China)2025

A perspective on developing foundation models for analyzing spatial transcriptomic data.

Tianyu Liu, Minsheng Hao, Xinhao Liu, Hongyu Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Quantitative biology (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
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

4 authors.

Tianyu LiuInterdepartmental Program of Computational Biology and Bioinformatics Yale University New Haven Connecticut USA.
Minsheng HaoResearch and Early Development Genentech South San Francisco California USA.
Xinhao LiuDepartment of Computer Science Princeton University Princeton New Jersey USA.
Hongyu ZhaoInterdepartmental Program of Computational Biology and Bioinformatics Yale University New Haven Connecticut USA.

Funding

Computational and Statistical Methods to determine variant effect across cell types and development stagesU01HG013840 · NHGRI · YALE UNIVERSITY · PI GERSTEIN, MARK BENDER, ZHAO, HONGYU · 2024 to 2024
$1.9M
NHGRI NIH HHS U01 HG013840
6 · The paper itself

Abstract

Do we need a foundation model (FM) for spatial transcriptomic analysis? To answer this question, we prepared this perspective as a primer. We first review the current progress of developing FMs for modeling spatial transcriptomic data and then discuss possible tasks that can be addressed by FMs. Finally, we explore future directions of developing such models for understanding spatial transcriptomics by describing both opportunities and challenges. In particular, we expect that a successful FM should boost research productivity, increase novel biological discoveries, and provide user-friendly access.

Indexed as

artificial intelligencefoundation modelsperspectivespatial transcriptomics data

Identifiers

PMID41674716
PMCPMC12806139

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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 OpenQuestion graph.