Results 91 to 100 of about 83,537 (273)
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction
G3: Genes, Genomes, Genetics, 2017 Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main ...
Massaine Bandeira e Sousa +7 more
doaj +1 more source
"Theory of Linear Mixed Models and its Applications to Small Area Estimation"(in Japanese) [PDF]
Linear mixed models (LMM) and the best linear unbiased predictor (BLUP) have received considerable attention in recent years from both theoretical and practical aspects.
Tatsuya Kubokawa
core
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Revista Colombiana de Estadística, 2007 The mixed linear model is characterized using the classic linear model of Gauss-Markov. The multipliers of Lagrange are a tool to obtain the best lineal predictors (BLUP), we shown the results of Searle (1997), where some sums of the best linear unbiased
López Luis Alberto +2 more
doaj
Bootstrap for estimating the mean squared error of the spatial EBLUP [PDF]
This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared error ...
Isabel Molina +2 more
core
Journal of Multivariate Analysis, 1992 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kleffe, J, Rao, J.N.K
openaire +2 more sources
Advanced Science, EarlyView.The authors complement bovine pan‐SV with massive novel structural variations (SVs) identified through long‐read sequencing of 83 globally distributed cattle breeds. Repetitive sequence‐mediated SVs (rep‐SV) exhibit distinct dynamic patterns throughout cattle sub‐speciation and/or domestication processes, including uneven distribution between chr‐X and
Zhifan Guo +16 more
wiley +1 more source
Revista Colombiana de Estadística, 2007 A través del modelo lineal clásico de Gauss-Markov, se caracteriza el modelo de efectos mixtos, se aplica la técnica de multiplicadores de Lagrange para obtener los mejores predictores lineales (BLUP) y se ilustran los resultados de Searle (1997), donde ...
LUIS ALBERTO LÓPEZ +2 more
doaj
Built-in Restrictions on Best Linear Unbiased Predictors (BLUP) of Random Effects in Mixed Models [PDF]
The American Statistician, 1997 Abstract In the usual mixed model of analysis of variance we show that certain sums of best linear unbiased predictors (BLUP) of random effects are zero. Those sums are similar to, but not exactly the same as, those of the Σ-restrictions sometimes used for fixed effects.
openaire +1 more source
Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES
Advanced Science, EarlyView.In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu +5 more
wiley +1 more source