Evidence map›Paper›PMID 41578913›Full record

ArticleThe plant genome2026

Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study.

Wei Zhou, Haoyong Ouyang, Zhengxiao Yan, Jiayi Song, Yichan Li, Zhou Tang, Chang Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The plant genome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Wei ZhouCollege of Agriculture and Food Science, Florida Agricultural and Mechanical University, Tallahassee, Florida, USA.ORCID https://orcid.org/0000-0001-8448-5540
Haoyong OuyangCollege of Agriculture and Food Science, Florida Agricultural and Mechanical University, Tallahassee, Florida, USA.
Zhengxiao YanDepartment of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Jiayi SongSchool of Natural Resources and Environment, University of Florida, Gainesville, Florida, USA.
Yichan LiDepartment of Geosciences, University of Arkansas, Fayetteville, Arkansas, USA.
Zhou TangAgronomy Department, University of Florida, Gainesville, Florida, USA.
Chang ZhaoAgronomy Department, University of Florida, Gainesville, Florida, USA.ORCID https://orcid.org/0000-0002-5389-0479

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To improve the identification of minor-effect molecular markers and genes associated with quantitative traits, addressing inefficiencies in traditional molecular marker mining and the limited impact of these markers in practical breeding, we analyzed over 15,000 soybean (Glycine max) genotypes across nine maturity groups using an AI-driven genomic approach, uncovering 2513 key genetic markers and 393 genes associated with yield. These included stable determinants unaffected by environmental variability and others with environment-dependent effects. The light gradient boosting machine (LightGBM) model was applied, and model performance was assessed using multiple evaluation metrics across the total datasets by fivefold cross-validation, yielding a mean squared error of 0.0862 and an R

Indexed as

Glycine maxMachine LearningQuantitative Trait LociBoosting Machine Learning AlgorithmsGenetic MarkersGenotypePlant BreedingPrediction AlgorithmsGenetic Markers

Identifiers

PMID41578913
PMCPMC12834258

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.