Results 21 to 30 of about 216,838 (308)

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

open access: yesFrontiers in Water, 2023
Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use ...
Mohammad Zeynoddin   +2 more
doaj   +1 more source

Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study

open access: yesAnimals, 2023
As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic ...
Shinichiro Ogawa   +3 more
doaj   +1 more source

Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation [PDF]

open access: yesAnimal Bioscience
Objective This study assessed the impact of incorporating imputed single nucleotide polymorphism (SNP) information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation ...
Danai Jattawa   +3 more
doaj   +1 more source

Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort

open access: yesMetabolites, 2022
Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association ...
Eva König   +11 more
doaj   +1 more source

Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits

open access: yesAnimals, 2021
Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits.
Enrico Mancin   +3 more
doaj   +1 more source

Design of an imputation methodology by random selection using regression trees

open access: yesBulletin of Computational Applied Mathematics, 2021
One of the biggest issues in the information collection stage is the absence of data, this research focuses specifically on the scenario when the loss is partial, completely random and the data is quantitative. There are classic techniques to impute data,
Lelly Useche   +3 more
doaj  

Completing a molecular timetree of apes and monkeys

open access: yesFrontiers in Bioinformatics, 2023
The primate infraorder Simiiformes, comprising Old and New World monkeys and apes, includes the most well-studied species on earth. Their most comprehensive molecular timetree, assembled from thousands of published studies, is found in the TimeTree ...
Jack M. Craig   +12 more
doaj   +1 more source

Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle

open access: yesAnimal, 2013
Imputation of high-density genotypes from low- or medium-density platforms is a promising way to enhance the efficiency of whole-genome selection programs at low cost.
Z. Weng   +5 more
doaj   +1 more source

Genomic inbreeding coefficients using imputed genotypes: Assessing different estimators in Holstein-Friesian dairy cows

open access: yesJournal of Dairy Science, 2022
: The objective of this study was to estimate inbreeding coefficients in Holstein dairy cattle using imputed SNPs data. A data set of 95,540 Italian Holstein dairy cows from the routine genomic evaluations of the Italian National Association of Holstein,
Christos Dadousis   +7 more
doaj   +1 more source

Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. [PDF]

open access: yes, 2012
BACKGROUND: Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates.
Bartlett Jonathan W   +8 more
core   +1 more source

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