Evidence map›Paper›PMID 41560909›Full record

ArticleFrontiers in plant science2025

MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning.

Freddy Mora-Poblete, Javiera Valenzuela-Herrera, Matías Balach, Claudio J Martínez-Araya, Carlos Maldonado

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2025. 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

5 authors.

Freddy Mora-PobleteInstitute of Biological Sciences, University of Talca, Talca, Chile.
Javiera Valenzuela-HerreraCentro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Huechuraba, Santiago, Chile.
Matías BalachCentro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Huechuraba, Santiago, Chile.
Claudio J Martínez-ArayaCentro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Huechuraba, Santiago, Chile.
Carlos MaldonadoCentro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Huechuraba, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the

Indexed as

deep learninggenomic selectionhyperparameters optimizationmolecular markersmulti-traits and multi-environmentsspectral information

Identifiers

PMID41560909
PMCPMC12812867

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.