Evidence map›Paper›PMID 37614817›Full record

ArticleFrontiers in genetics2023

The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction.

Darcy Jones, Roberta Fornarelli, Mark Derbyshire, Mark Gibberd, Kathryn Barker, James Hane

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2023. 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. 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.

Darcy JonesCentre for Crop and Disease Management, Curtin University, Perth, WA, Australia.
Roberta FornarelliCentre for Crop and Disease Management, Curtin University, Perth, WA, Australia.
Mark DerbyshireCentre for Crop and Disease Management, Curtin University, Perth, WA, Australia.
Mark GibberdCentre for Crop and Disease Management, Curtin University, Perth, WA, Australia.
Kathryn BarkerCurtin Institute for Computation, Curtin University, Perth, WA, Australia.
James HaneCentre for Crop and Disease Management, Curtin University, Perth, WA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current practice in agriculture applies genomic prediction to assist crop breeding in the analysis of genetic marker data. Genomic selection methods typically use linear mixed models, but using machine-learning may provide further potential for improved selection accuracy, or may provide additional information. Here we describe SelectML, an automated pipeline for testing and comparing the performance of a range of linear mixed model and machine-learning-based genomic selection methods. We demonstrate the use of SelectML on an

Indexed as

crop improvementgenetic gaingenomic predictionlinear-mixed modelsmachine-learning

Identifiers

PMID37614817
PMCPMC10443705

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.