Design and Testing of Root-Specific Synthetic Promoters by Machine Learning.

Abstract read

In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026
Review

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

3 authors.

Chunhao LuState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

Yuepeng SongState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0001-8946-6437

Deqiang ZhangState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0002-8849-2366

Funding

the 111 Project B20050the Project of the National Natural Science Foundation of China 32370396, and 32570425the State "14.5" Key Research Program of China 2021YFD2200101

6 · The paper itself

Abstract

Synthetic promoters are crucial for precise gene expression in transgenic plants, but their rational design is hindered by the difficulty in identifying functional cis-regulatory elements (CREs). In this study, we aimed to develop a systematic approach for discovering tissue-specific cis-regulatory modules (CRMs) and generating functional synthetic promoters in poplar. We performed extensive transcriptomic analysis across various poplar tissues to obtain categorical labels and detected motifs in all gene promoters using known transcription factor binding site (TFBS) position weight matrices. Informative, tissue-specific TFBSs were predicted using a random forest model. Applying this to a root-specific gene,

Indexed as

Machine LearningPlant RootsPopulusPromoter Regions, GeneticBinding SitesGene Expression Regulation, PlantPlant ProteinsPlants, Genetically ModifiedTranscription FactorsPlant ProteinsTranscription Factorscis-regulatory modulemachine learningPopulussynthetic promoter

Identifiers

PMID41898404

DOI10.3390/ijms27062540

PMCPMC13026494

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.