Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

PloS one - 1 Jan 2021

da Silva Júnior Antônio Carlos, Silva Michele Jorge da, Cruz Cosme Damião, Sant'Anna Isabela de Castro, Silva Gabi Nunes, Nascimento Moysés, Azevedo Camila Ferreira

Abstract excerpt

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artificial Intelligence
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Linkage

Share to Genotype

Share to Machine Learning

Share to Phenotype

Share to Plant Breeding

Share to Quantitative Trait Loci

Source

Open in PubMed Open full text

Identifiers

PMID: 34843488
DOI: 10.1371/journal.pone.0257213
PMCID: PMC8629227

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.PMID 34843488Read ↗

From association to prediction: statistical methods for the dissection and selection of complex traits in plants.Semantic proximity Read ↗

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.Semantic proximity Read ↗

Connecting mathematical models to genomes: joint estimation of model parameters and genome-wide marker effects on these parameters.Semantic proximity Read ↗

Models for navigating biological complexity in breeding improved crop plants.Semantic proximity Read ↗

Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.Semantic proximity Read ↗

Artificial intelligence in plant breeding.Semantic proximity Read ↗

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge.Semantic proximity Read ↗

Epistasis and pleiotropy-induced variation for plant breeding.Semantic proximity Read ↗

Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding.Semantic proximity Read ↗

Using machine learning and partial dependence to evaluate robustness of best linear unbiased prediction (BLUP) for phenotypic values.Semantic proximity Read ↗

Genome-wide estimation of heritability and its functional components for flowering, defense, ionomics, and developmental traits in a geographically diverse population of Arabidopsis thaliana.Semantic proximity Read ↗

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.