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

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

PloS one - 1 Jan 2022

Coelho de Sousa Ithalo, Nascimento Moysés, de Castro Sant'anna Isabela, Teixeira Caixeta Eveline, Ferreira Azevedo Camila, Damião Cruz Cosme, Lopes da Silva Felipe, Ruas Alkimim Emilly, Campana Nascimento Ana Carolina, Vergara Lopes Serão Nick

Abstract excerpt

Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and...

Read the complete abstract on PubMed

Topics

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

Share to Coffea
Share to Genetic Markers

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome, Plant

Share to Genomics

Share to Genotype

Share to Models, Genetic

Share to Neural Networks, Computer

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 35081139
DOI: 10.1371/journal.pone.0262055
PMCID: PMC8791507

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

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.PMID 35081139Read ↗

Bayesian neural networks with variable selection for prediction of genotypic values.Semantic proximity Read ↗

Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.Semantic proximity Read ↗

Genome-wide association study of plant architecture and diseases resistance in Coffea canephoraSemantic proximity Read ↗

Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.Semantic proximity Read ↗

Genome-wide association analysis of traits related to development, abiotic and biotic stress resistance in Coffea canephoraSemantic proximity Read ↗

Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits.Semantic proximity Read ↗

Genomic‐assisted breeding for climate‐smart coffeeSemantic proximity Read ↗

Genomic insights into population structure and predictive breeding for climate-resilient coffee.Semantic proximity Read ↗

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.Semantic proximity Read ↗

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects.Semantic proximity Read ↗

Genome-enabled prediction using probabilistic neural network classifiers.Semantic proximity Read ↗

Enhancing grapevine breeding efficiency through genomic prediction and selection index.Semantic proximity Read ↗

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