Evidence map›Paper›PMID 39882073›Full record

ArticleFrontiers in genetics2024

Classical and machine learning tools for identifying yellow-seeded

Fan Liu, Fang Wang, Zaiqi Zhang, Liang Cao, Jinran Wu, You-Gan Wang

Abstract read
PubMed Full text at PMC Publisher
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Article in Frontiers in genetics, 2024. 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.

  1. Unraveling the complexity of the oilseed rape genome: from sequencing to gene discovery for trait improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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

6 authors.

Fan LiuHunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
Fang WangKey Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China.
Zaiqi ZhangHunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
Liang CaoHunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
Jinran WuSchool of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia.
You-Gan WangSchool of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Due to its favorable traits-such as lower lignin content, higher oil concentration, and increased protein levels-the genetic improvement of yellow-seeded rapeseed has attracted more attention than other rapeseed color variations. Traditionally, yellow-seeded rapeseed has been identified visually, but the complex variability in the seed coat color of Methods: We present four data-driven models to identify yellow-seeded Results and Discussion: Our findings indicate significant recognition accuracies of 96.55% and 98% for the PLSR and Logit-R models, respectively, aligning closely with the accuracy of previous methods. This approach represents a meaningful advancement in identifying yellow-seeded rapeseed, with high recognition accuracy demonstrating the practical applicability of these models.

Indexed as

hyperspectral featurelogistic regressionmachine learningpartial least squares regressionrapeseed (Brassica napus)yellow-seeded

Identifiers

PMID39882073
PMCPMC11774891

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.