Evidence map›Paper›PMID 42449400›Full record

ArticleGenome medicine2026

Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts.

Zijun Wang, Chongyi Yang, Xiaoya Tang, Enzhi Yin, Yuxin Yao, Yuejun Luo, Jie He, Nan Sun

Abstract read
PubMed Publisher
In one paragraph

Article in Genome medicine, 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

8 authors.

Zijun Wang *Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China.ORCID http://orcid.org/0009-0003-2531-5818
Chongyi Yang *National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China.
Xiaoya TangDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China.
Enzhi YinDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China.
Yuxin YaoDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China.
Yuejun LuoDepartment of Orthopaedic Oncology Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China.
Jie HeDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China. prof.jiehe@gmail.com.
Nan SunDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P.R. China. sunnan@cicams.ac.cn.ORCID https://orcid.org/0000-0001-6812-9917

Funding

Beijing Municipal Natural Science Foundation JQ25027Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0533300
6 · The paper itself

Abstract

Spatial transcriptomics is powerful but costly; hematoxylin and eosin (H&E) images are routine. We present Coladan-human3K, the largest human spatial transcriptomics resource (~ 3,000 profiles), and Coladan, a trimodal (image, language, spatial-gene) whole-slide framework predicting genome-wide genes per spot with calibrated uncertainty while preserving foundation-model representations. Across 32 Visium datasets, Coladan improves Pearson correlation from 0.230 to 0.431 (~ 1.9 ×), shows pathway-level enrichment consistency, and transfers zero-shot to VisiumHD and spot-level Xenium. Classification token (CLS) embedding-only perturbation performs on par with expression-based baselines, enabling image-only virtual perturbation without measured expression, illustrated on normal and cancer prostate sections for in-situ hypothesis generation.

Indexed as

Gene Expression ProfilingImage Processing, Computer-AssistedNeoplasmsHumansMaleProstatic NeoplasmsSpatial TranscriptomicsUncertaintyDigital pathologyGenome-wide expressionMixture of expertsMultimodal modelsSpatial transcriptomicsUncertainty estimationVirtual perturbationWhole-slide images

Identifiers

PMID42449400

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.