Results 51 to 60 of about 658,808 (305)
Some remarks on comparison of predictors in seemingly unrelated linear mixed models [PDF]
summary:In this paper, we consider a comparison problem of predictors in the context of linear mixed models. In particular, we assume a set of $m$ different seemingly unrelated linear mixed models (SULMMs) allowing correlations among random vectors ...
Eriş Büyükkaya, Melek, Güler, Nesrin
core +1 more source
Reciprocal control of viral infection and phosphoinositide dynamics
Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley +1 more source
mplot: An R Package for Graphical Model Stability and Variable Selection Procedures
The mplot package provides an easy to use implementation of model stability and variable inclusion plots (Müller and Welsh 2010; Murray, Heritier, and Müller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao ...
Garth Tarr +2 more
doaj +1 more source
lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models
Background Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity.
Brian E. Vestal +2 more
doaj +1 more source
Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source
Multivariate Generalized Linear Mixed Models for Count Data
Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of ...
Guilherme Parreira da Silva +4 more
doaj +1 more source
Identifying genetically driven clinical phenotypes using linear mixed models
Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait.
Jonathan D. Mosley +13 more
doaj +1 more source
Non-linear Mixed Models in a Dose Response Modelling
Study designs in which an outcome is measured more than once from time to time result in longitudinal data. Most of the methodological works have been done in the setting of linear and generalized linear models, where some amount of linearity is retained.
Madona Yunita Wijaya
doaj +1 more source
Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies.
Eiji Yamamoto, Hiroshi Matsunaga
doaj +1 more source
The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source

