Cargando…
Genome-wide prediction using Bayesian additive regression trees
BACKGROUND: The goal of genome-wide prediction (GWP) is to predict phenotypes based on marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The major problem with GWP is high-dimensional data from many thousands of SNPs scored on several thousands of individuals. A la...
Autor principal: | Waldmann, Patrik |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4901500/ https://www.ncbi.nlm.nih.gov/pubmed/27286957 http://dx.doi.org/10.1186/s12711-016-0219-8 |
Ejemplares similares
-
Approximate Bayesian neural networks in genomic prediction
por: Waldmann, Patrik
Publicado: (2018) -
Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA
por: Sparapani, Rodney, et al.
Publicado: (2019) -
Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis
por: Hu, Wenbiao, et al.
Publicado: (2011) -
Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees
por: Li, Li, et al.
Publicado: (2019) -
Genome-wide prediction of discrete traits using bayesian regressions and machine
learning
por: González-Recio, Oscar, et al.
Publicado: (2011)