Evidence map›Paper›PMID 39202329›Full record

ArticleGenes2024

A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets.

Osval A Montesinos-López, Cristian Daniel Pulido-Carrillo, Abelardo Montesinos-López, Jesús Antonio Larios Trejo, José Cricelio Montesinos-López, Afolabi Agbona, José Crossa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2024. 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.

Osval A Montesinos-LópezFacultad de Telemática, Universidad de Colima, Colima 28040, Mexico.
Cristian Daniel Pulido-CarrilloFacultad de Telemática, Universidad de Colima, Colima 28040, Mexico.
Abelardo Montesinos-LópezCentro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Mexico.
Jesús Antonio Larios TrejoFacultad de Ciencias de la Educación, Universidad de Colima, Colima 28040, Mexico.
José Cricelio Montesinos-LópezDepartment of Public Health Sciences, University of California Davis, Davis, CA 95616, USA.ORCID 0000-0001-5017-6192
Afolabi AgbonaInternational Institute of Tropical Agriculture (IITA), Ibadan 200113, Nigeria.
José CrossaInternational Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, Texcoco 52640, Mexico.

Funding

Bill and Melinda Gates Foundation [INV-003439, BMGF/FCDO, Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW)].
6 · The paper itself

Abstract

Genomic selection (GS) is changing plant breeding by significantly reducing the resources needed for phenotyping. However, its accuracy can be compromised by mismatches between training and testing sets, which impact efficiency when the predictive model does not adequately reflect the genetic and environmental conditions of the target population. To address this challenge, this study introduces a straightforward method using binary-Lasso regression to estimate

Indexed as

GenomicsModels, GeneticPlant BreedingGenome, PlantPhenotypeRegression AnalysisSelection, GeneticElastic Net regressiongenomic selectionLasso regressionmismatchRidge regressionweighted regression

Identifiers

PMID39202329
PMCPMC11353568

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.