Evidence map›Paper›PMID 42197712›Full record

ArticlePlants (Basel, Switzerland)2026

Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.

Hui Shen, Wen Liu, Yuanheng Li, Zhaoyilan He, Zheng'an Yang, Zongli Hu, Ting Wu

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 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

7 authors.

Hui ShenKey Laboratory of Vegetable Biology of Yunnan Province, College of Landscape and Horticulture, Yunnan Agricultural University, No. 452, Fengyuan Road, Panlong District, Kunming 650201, China.ORCID 0000-0002-1082-0155
Wen LiuKey Laboratory of Vegetable Biology of Yunnan Province, College of Landscape and Horticulture, Yunnan Agricultural University, No. 452, Fengyuan Road, Panlong District, Kunming 650201, China.
Yuanheng LiKey Laboratory of Vegetable Biology of Yunnan Province, College of Landscape and Horticulture, Yunnan Agricultural University, No. 452, Fengyuan Road, Panlong District, Kunming 650201, China.
Zhaoyilan HeKey Laboratory of Vegetable Biology of Yunnan Province, College of Landscape and Horticulture, Yunnan Agricultural University, No. 452, Fengyuan Road, Panlong District, Kunming 650201, China.
Zheng'an YangKey Laboratory of Vegetable Biology of Yunnan Province, College of Landscape and Horticulture, Yunnan Agricultural University, No. 452, Fengyuan Road, Panlong District, Kunming 650201, China.
Zongli HuLaboratory of Molecular Biology of Tomato, Bioengineering College, Chongqing University, Chongqing 400030, China.
Ting WuKey Laboratory of Vegetable Biology of Yunnan Province, College of Landscape and Horticulture, Yunnan Agricultural University, No. 452, Fengyuan Road, Panlong District, Kunming 650201, China.

Funding

Key Laboratory of Vegetable Biology of Yunnan Province A3032024070Less Developed Regions of the National Natural Science Foundation of China 32560740Major Program of the National Natural Science Foundation of Yunnan Province A3032025196Yunnan Fundamental Research Projects 202501AT070147
6 · The paper itself

Abstract

High-throughput single-cell RNA sequencing (scRNA-seq) has substantially advanced plant transcriptional landscapes. However, decoding cell-type-specific transcriptional regulation in non-model crops like tomato (

Indexed as

hdWGCNAmachine learningsingle-cell RNA sequencingtomato leaftranscription factors (TFs)

Identifiers

PMID42197712
PMCPMC13210926

What OpenQuestion holds

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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.