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

Evaluation of random forest regression for prediction of breeding value from genomewide SNPs.

Journal of genetics - 1 Jun 2015

Sarkar Rupam Kumar, Rao A R, Meher Prabina Kumar, Nepolean T, Mohapatra T

Abstract excerpt

Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Breeding
Share to Databases, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome, Plant

Share to Models, Genetic

Share to Polymorphism, Single Nucleotide

Share to Regression Analysis

Share to Zea mays

Source

Identifiers

PMID: 26174666
DOI: 10.1007/s12041-015-0501-5

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

Evaluation of random forest regression for prediction of breeding value from genomewide SNPs.PMID 26174666Read ↗

Assessing the performance of a novel method for genomic selectio:rrBLUP-method6.Semantic proximity Read ↗

Comparisons of single-stage and two-stage approaches to genomic selection.Semantic proximity Read ↗

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.Semantic proximity Read ↗

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Semantic proximity Read ↗

Sample size determination for training set optimization in genomic prediction.Semantic proximity Read ↗

Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait.Semantic proximity Read ↗

Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Semantic proximity Read ↗

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

Ensemble learning for integrative prediction of genetic values with genomic variants.Semantic proximity Read ↗

Accuracy and bias of genomic prediction with different de-regression methods.Semantic proximity Read ↗

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F2 populations by using genomic selection models.Semantic proximity Read ↗

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