Results 141 to 150 of about 2,698,368 (301)

Oncogenic DMTF1β promotes cancer cell motility by regulating autophagy through ULK1 stabilization

open access: yesMolecular Oncology, EarlyView.
In the current study, we demonstrate that the oncogene DMTF1β regulates ULK1 stability by reducing its proteasomal degradation in cancer cells. This stabilization enables ULK1 to induce autophagy, which in turn facilitates cancer cell migration. Consequently, reduced DMTF1β levels lead to decreased autophagy and impaired cancer cell migration.
Jun Xu   +13 more
wiley   +1 more source

Hierarchical Generalized Linear Models: The R Package HGLMMM

open access: yesJournal of Statistical Software, 2011
The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution.
Marek Molas, Emmanuel Lesaffre
doaj  

A Bayesian adjustment of the modified profile likelihood

open access: yes, 2011
We propose an adjustment of the modified profile likelihood based on a suitable matching prior on the parameter of interest only, i.e. a prior for which there is an agreement between frequentist and Bayesian inference.We show that the proposed modified ...
W. RACUGNO, VENTURA, LAURA
core  

Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer

open access: yesMolecular Oncology, EarlyView.
Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola   +11 more
wiley   +1 more source

Corrected modified profile likelihood heteroskedasticity tests

open access: yes
This paper considers the issue of testing for the constancy of variances in linear regression models. We obtain a Bartlett correction to the modified profile likelihood ratio test proposed by Simonoff and Tsai (Appl. Statist. 43 (1994) 357) who have used
Cribari-Neto, Francisco   +1 more
core  

PAK1 activation drives divergent resistance mechanisms to aromatase inhibition and tamoxifen in a luminal: A breast cancer model

open access: yesMolecular Oncology, EarlyView.
Breast cancer remains a major cause of cancer death in women, frequently developing endocrine therapy resistance. This study demonstrates that upregulated p21‐activated kinase 1 (PAK1) activity drives resistance to tamoxifen and long‐term estrogen deprivation in ER+ breast cancer models.
Luisa Schwarzmüller   +10 more
wiley   +1 more source

A robust adjustment of the profile likelihood

open access: yesThe Annals of Statistics, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Spatial and single‐nuclei transcriptomics reveals idiosyncratic and generic patterns in papillary and anaplastic thyroid cancers

open access: yesMolecular Oncology, EarlyView.
Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur   +11 more
wiley   +1 more source

Profile Likelihood Estimation of the Vulnerability P(X\textgreater v) and the Mixing Proportion p Parameters in the Gumbel Mixture Model

open access: yesRevista Colombiana de Estadística, 2013
This paper concerns to the problem of making inferences about the vulnerability θ=P(X\textgreater v) and the mixing proportion p parameters, when the random variable X is distributed as a mixture of two Gumbel distributions and v is a known fixed value ...
JOSÉ A. MONTOYA   +2 more
doaj  

Likelihood based estimation

open access: yes, 2023
In this thesis we will describe the maximum likelihood method, method of estima- ting unknown parameters that determine the probability distribution of the observed data. We will also introduce other methods derived from the likelihood.
Březinová, Eva
core  

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